Enhancing the tonal characteristics of digital images using inflection points in a tone scale function

ABSTRACT

A method of producing a tone scale function which operates on a source digital image to improve tonal characteristics, includes receiving a source digital image including a plurality of pixels; and producing a tone scale function having a highlight tone scale segment and a shadow tone scale segment defined relative to a reference point on the tone scale function, and that is adapted to operate on the source digital image, wherein the highlight tone scale segment is defined by a different mathematical function than the shadow tone scale segment; at least one of the highlight tone scale or shadow tone scale segments have at least one inflection point; the highlight tone scale segment is defined for points that are equal to or greater than the reference point; and the shadow tone scale segment is defined for points that are equal to or less than the reference point.

CROSS REFERENCE TO RELATED APPLICATION

Reference is made to commonly-assigned U.S. patent application Ser. No. 10/178,260, filed Jun. 24, 2002, entitled “Enhancing the Tonal Characteristics of Digital Images” by Edward B. Gindele, the disclosure of which is incorporated herein by reference.

FIELD OF THE INVENTION

This invention relates to digital image processing and in particular to processing a digital image to enhance its color, brightness, and tone scale characteristics.

BACKGROUND OF THE INVENTION

Many digital imaging systems enhance the contrast and lightness characteristics of digital images through the application of a tone scale curve. For a generalized tone scale curve ƒ( ), the input pixel value x is transformed to an output pixel value ƒ(x). The shape of the tone scale curve determines the visual effect imparted to the processed digital image. Some tone scale curves applied to digital image are independent of the pixel values in the digital image to be processed. Such image independent tone scale curves are useful for establishing a photographic look to the processed digital images. While image independent tone scale curves can be used to enhance many digital images, digital images that are either too high or low in contrast can benefit from the application of a tone scale curve that is responsive to the distribution of pixel values in the digital image to be processed. For image dependent tone scale curves, the mathematical formula used to generate the function ƒ(x) determines the degree and nature of the image enhancement.

One class of tone scale function generation methods is derived from histogram equalization. A histogram function H(x), i.e. a function of the frequency of occurrence, is calculated from the pixel values x of a digital image. Next the function ƒ(x) is determined for which the histogram function of the processed pixels H(ƒ(x)) will have a particular aim functional W(x). The function ƒ(x) that satisfies this constraint can be calculated given the expression ƒ(x)=kC(W¹(H(x))) where the variable k is a normalizing constant. For the special case where the aim functional W(x) is a constant, the expression for the tone scale curve ƒ(x) is given by the expression ƒ(x)=kC(H(x)).

There are many prior art examples of histogram equalization based methods: In commonly-assigned U.S. Pat. No. 4,731,671 Alkofer discloses a method of using a Gaussian function as the aim function W(x). In commonly-assigned U.S. Pat. No. 4,745,465 Kwon discloses a method of generating a tone scale curve also employing a histogram equalization derived method wherein a Gaussian function is used as the aim function W(x). In Kwon's method, the image histogram is calculated by sampling pixels within the image that have been classified as spatially active. An edge detection spatial filter is used to determine the degree of local spatial activity for each image pixel. The local spatial activity measure is compared with a threshold to determine if the pixel value will contribute to the histogram function used to generate the tone scale curve. As with less sophisticated histogram equalization based methods, the methods disclosed by Alkofer and Kwon suffer from inconsistent image enhancement performance. This is principally due to the fact that histogram equalization methods tend to optimize the visualization of image content based on the frequency of occurrence of the corresponding pixel values. As a consequence, extremely bright or dark image areas that are represented by a small percentage of image area can be overwhelmed by more prevalent image areas resulting in tone scale adjusted images that have specular highlights that are rendered too dark and deep shadows that are rendered too light. Therefore, histogram equalization based methods are more suited to image exploitation applications requiring the visualization of image detail than to applications involving the tone reproduction of natural scenes.

In commonly-assigned U.S. Pat. No. 6,285,798 Lee discloses a method of generating a tone scale curve for the purposes of reducing the dynamic range of a digital image. The tone scale curve construction method establishes six constraints and then performs a successive integration procedure to satisfy the constraints. In Lee's method, a dark point determined by the 0.5% image cumulative histogram function value is mapped to a white paper density, a bright point determined by the 99.5% image cumulative histogram function value is mapped to a black paper density, and a mid-point is mapped to itself. Next a shadow slope constraint of greater than 1.0 is imposed at the 0.5% shadow point, a highlight slope constraint of 1.0 is imposed at the 99.5% highlight point, and a mid-tone slope constraint of 1.0 is imposed at the mid-point. Lee states that there are an infinite number of tone scale curves that can satisfy the six constraints. Lee's method constructs a tone scale curve that satisfies the six constraints by assuming an arbitrary initial shape for the tone scale curve and successively convolving the tone scale curve with a Gaussian smoothing function until, upon examination, the tone scale curve satisfies the six constraints to within some acceptable tolerance. Lee's method does not discuss a closed form solution, i.e. a mathematical function that can be evaluated for each point, to the six constraints and therefore must rely the complicated integration procedure. The tone scale curves so constructed are smoothly varying achieving a high slope value at the extremes and at the mid-point with an inflection point between the mid-point and the highlight point and an inflection point between the mid-point and the shadow point. While Lee's method disclosed in commonly-assigned U.S. Pat. No. 6,285,798 can produce smoothly varying tone scale curves, the method does not always converge to a curve that satisfies the six constraints. Furthermore, the Lee's method does not account for the possibility that some digital images require an expansion of the dynamic range of the digital image to achieve enhancement. In addition, the high slope constraints imposed at the extremes and at the mid point of the pixel intensity domain can sometimes lead to a sacrifice of quality for image content corresponding to pixel values that lie between the shadow point and the mid-point, and the mid-point and the highlight point.

In the journal article entitled “Image lightness rescaling using sigmoidal contrast enhancement functions” published in the Journal of Electronic Images Vol. 8(4), p380–393 (October 1999), authors Braun et al. discuss a method of using a single sigmoidal function, e.g. as the integral-of-a-Gaussian function, as a method of generating a tone scale curve that can be used for contrast enhancement of digital images. The sigmoidal function presented by Braun et al. is controlled with a standard deviation and offset parameter which determine the shape of the function. The offset parameter is used to impart lightness changes to digital images while the standard deviation parameter is used to impart contrast changes. While the sigmoidal shaped tone scale curve generation method presented by Braun et al. provides photographically acceptable results, the shape of the function corresponding to shadow and highlight regions of images is not independently controllable. Consequently, for a given digital image, it can be difficult to achieve the desired degree of contrast enhancement while simultaneously achieving the optimum image lightness rendition.

SUMMARY OF THE INVENTION

It is an object of the present invention to improve the method for producing a tone scale function that can be used to enhance the tonal characteristics of a digital image.

This object is achieved by a method of producing a tone scale function which can operate on a source digital image to improve tonal characteristics, comprising the steps of:

a) receiving a source digital image including a plurality of pixels; and

b) producing a tone scale function having a highlight tone scale segment and a shadow tone scale segment defined relative to a reference point on the tone scale function, and that is adapted to operate on the source digital image to improve its tonal characteristics, wherein:

-   -   i) the highlight tone scale segment is defined by a different         mathematical function than the shadow tone scale segment;     -   ii) at least one of the highlight tone scale or shadow tone         scale segments have at least one inflection point;     -   iii) the highlight tone scale segment is defined for points that         are equal to or greater than the reference point; and     -   iv) the shadow tone scale segment is defined for points that are         equal to or less than the reference point.

By practicing the present invention, the characteristics of the highlight and shadow regions of the digital image are significantly enhanced. It is an important feature of the present invention that at least one of the highlight tone scale or shadow tone scale segments or both have at least one inflection point. A highlight inflection point allows the extreme specular highlight regions of images to rendered to a paper white density thus preserving the visual impression of specular highlights. Similarly, a shadow inflection point allows the darkest regions of images to rendered to a paper black density thus preserving the visual impression of deep shadows.

The present invention facilitates using tone scale functions to improve the rendition of highlight and shadow regions wherein the shape of the tone scale function achieves an inflection point independently for highlight and shadow image regions and such inflection points are independently controllable of each other.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram showing the component parts of a system implementation of the present invention;

FIG. 2 is a functional block diagram of the digital image processor;

FIG. 3 a is a graph of an example of a rendering function;

FIG. 3 b is a graph of another example of a rendering function;

FIG. 4 is a functional block diagram of the tone scale module;

FIG. 5 is a functional block diagram of another embodiment of the tone scale module;

FIG. 6 is a functional block diagram of embodiment of the tone scale function applicator;

FIG. 7. is a graph illustrating a family of highlight tone scale segments and a family of shadow tone scale segments;

FIG. 8 a is a graph of an example of a compressive highlight component function and its corresponding slope function;

FIG. 8 b is a graph of an example of a compressive shadow component function and its corresponding slope function;

FIG. 8 c is a graph of an example of a tone scale function constructed from a compressive highlight and shadow tone scale segment and its corresponding slope function;

FIG. 9 is a graph illustrating a family of highlight tone scale segments and a family of shadow tone scale segments constructed;

FIG. 10 a is a graph illustrating the construction details of an expansive highlight component function;

FIG. 10 b is a graph illustrating the construction details of an expansive shadow component function;

FIG. 10 c is a graph of an example of a tone scale function constructed from an expansive highlight and shadow tone scale segment and its corresponding slope function;

FIG. 11 is a graph illustrating a family of highlight tone scale segments and a family of shadow tone scale segments;

FIG. 12 a is a graph illustrating a family of highlight tone scale segments and a family of shadow tone scale segments for both compressive and expansive type functions;

FIG. 12 b is another graph illustrating a family of highlight tone scale segments and a family of shadow tone scale segments for both compressive and expansive type functions;

FIG. 13 a is a graph illustrating an example of a tone scale function wherein the highlight tone scale segment and the shadow tone scale segment are each constructed with two component functions and each component tone scale function has an inflection point;

FIG. 13 b is a graph illustrating the example tone scale function depicted in FIG. 12 a and the corresponding slope function wherein each component tone scale function has an inflection point;

FIG. 13 c. is a graph illustrating the details of the corresponding slope function depicted if FIG. 12 b;

FIG. 14 is a graph illustrating an example image histogram function and its corresponding cumulative histogram function;

FIG. 15 is a graph illustrating an example of the shape of the third shadow component function having an inflection point; and

FIG. 16 is a graph illustrating an example of a tone scale function wherein the shadow tone scale segment is each constructed with three component functions, with each component function having an inflection point.

DETAILED DESCRIPTION OF THE INVENTION

In the following description, a preferred embodiment of the present invention will be described as a software program. Those skilled in the art will readily recognize that the equivalent of such software may also be constructed in hardware. Because image processing algorithms and systems are well known, the present description will be directed in particular to algorithms and systems forming part of, or cooperating more directly with, the method in accordance with the present invention. Other aspects of such algorithms and systems, and hardware and/or software for producing and otherwise processing the image signals involved therewith, not specifically shown or described herein, may be selected from such systems, algorithms, components and elements thereof known in the art. Given the description as set forth in the following specification, all software implementation thereof as a computer program is conventional and within the ordinary skill in such arts.

Still further, as used herein, the computer program may be stored in a computer readable storage medium, which may comprise, for example: magnetic storage media such as a magnetic disk (such as a floppy disk) or magnetic tape; optical storage media such as an optical disc, optical tape, or machine readable bar code; solid state electronic storage devices such as random access memory (RAM), or read only memory (ROM); or any other physical device or medium employed to store a computer program.

A digital image is comprised of one or more digital image channels. Each digital image channel is comprised of a two-dimensional array of pixels. Each pixel value relates to the amount of light received by the imaging capture device corresponding to the geometrical domain of the pixel. For color imaging applications, a digital image will typically consist of red, green, and blue digital image channels. Other configurations are also practiced, e.g. cyan, magenta, and yellow digital image channels. For monochrome applications, the digital image consists of one digital image channel. Motion imaging applications can be thought of as a time sequence of digital images. Those skilled in the art will recognize that the present invention can be applied to, but is not limited to, a digital image channel for any of the above mentioned applications.

Although the present invention describes a digital image channel as a two-dimensional array of pixels values arranged by rows and columns, those skilled in the art will recognize that the present invention can be applied to mosaic (non-rectilinear) arrays with equal effect.

The present invention may be implemented in computer hardware. Referring to FIG. 1, the following description relates to a digital imaging system which includes an image capture device 10, a digital image processor 20, image output devices 30 a and 30 b, and a general control computer 40. The system may include a monitor device 50 such as a computer console or paper printer. The system may also include an input control device 60 for an operator such as a keyboard and or mouse pointer. Still further, as used herein, the present invention may be implemented as a computer program and may be stored in a computer memory device 70, i.e. a computer readable storage medium, which may comprise, for example: magnetic storage media such as a magnetic disk (such as a floppy disk) or magnetic tape; optical storage media such as an optical disc, optical tape, or machine readable bar code; solid state electronic storage devices such as random access memory (RAM), or read only memory (ROM); or any other physical device or medium employed to store a computer program. Before describing the present invention, it facilitates understanding to note that the present invention is preferably utilized on any well known computer system, such as a personal computer.

Multiple image capture devices 10 a, 10 b, and 10 c are shown illustrating that the present invention can be used for digital images derived from a variety of imaging devices. For example, FIG. 1 can represent a digital photofinishing system where the image capture device 10 a may be a film scanner device which produces digital images by scanning a conventional photographic image, e.g. color negative film or slide film transparencies. Similarly, image capture device 10 b could be a digital camera. The digital image processor 20 provides the means for processing the digital images to produce pleasing looking images on an intended output device or media. Multiple image output devices 30 a and 30 b are shown illustrating that the present invention may be used in conjunction with a variety of output devices which may include a digital photographic printer and soft copy display. It should also be noted that the present invention can be implemented within the hardware and software of a digital camera such that the digital images produced by the digital camera have been processed with the present invention prior to being exported by the digital camera.

The digital image processor 20 depicted in FIG. 1 is illustrated in more detail in FIG. 2. The cascaded image processing modules shown in FIG. 2 represents a digital image processing path. The original digital image 101 is received from one of the image input devices shown in FIG. 1 and processed to produce a rendered digital image 103 that can be realized on an image output device. The RLSE (relative log scene exposure) conversion module 310 receives the original digital image 101 and generates an RLSE digital image that is prepared for balancing and the application of a tone scale function. The scene balance module 320 receives the RLSE digital image and performs a lightness and color balance adjustment resulting in a source digital image 102. The tone scale module 330 receives the source digital image, generates a tone scale function from the source digital image and applies the tone scale function to the source digital image resulting in an enhanced digital image. If the RLSE conversion module 310 and the scene balance module 320 are omitted from the image processing path of modules, the tone scale module 330 receives the original digital image 101 directly as the source digital image. The rendering module 340 processes the enhanced digital image such that the pixel values of the generated rendered digital image 103 have been prepared to yield a pleasing result when used in conjunction with an output image device such as a digital printer. As part of the processing performed by the rendering module 340, a rendering function R(x) is applied to the pixel data of the enhanced digital image to achieve the input to output pixel mapping required of the output image device. The configuration of processing modules shown in FIG. 2 is used for processing digital images that are either monochrome or color in nature. That is, the original digital image 101 includes one or more digital image channels of pixels wherein each digital image channel relates to a different color or pixels such as, an original digital image 101 that includes red, green, and blue representation or a luminance-chrominance representation.

The tone scale functions produced by the present invention are designed to work best with digital images that are in a relative log scene exposure representation (RLSE). That is, the pixels of the original digital image 101 have a logarithmic relationship with respect to the original scene intensities of light from which the original digital image 101 is derived. For example, the image capture device 10 a shown in FIG. 1 can represent a photographic film scanner that produces digital images that have a linear or logarithmic relationship with the amount of transmitted light projected through a photographic negative or positive film transparency sample. If the pixels values of the resultant original digital image 101 have a logarithm relationship with the received light (i.e. the pixels are in a density representation), the original digital image 101 is considered to be in a relative log scene exposure representation. If the pixels of the original digital image 101 have a linear relationship with the received light, the original digital image 101 is considered to be in a linear exposure representation since, to within a reasonable approximation, the amount of light transmitted through the photographic film sample is linearly proportional to the amount of light received from the original photographed scene. A linear exposure representation digital image can be transformed into a relative log scene exposure representation by a linear-to-logarithmic transform implemented as function or as look-up-table (LUT) with the RLSE module 310 shown in FIG. 2.

The most common representation of digital images produced with digital cameras is a rendered representation, i.e. the digital image has been processed such that it will be yield a natural appearing image on an electronic display device. For most digital images produced with digital cameras the intended output image device is a CRT monitor device sometimes referred to as a gamma domain representation. Rendered digital images can also be transformed into a relative log scene exposure representation using a LUT transform. For digital images produced with digital cameras, the transform includes an inverse of the power law function associated with the intended electronic display device followed by an inverse rendering tone scale function related to the rendering tone scale function that the digital camera used to produce the original digital image 101. Alternatively, the method disclosed by McCarthy et al. in commonly-assigned U.S. Pat. No. 6,282,311 can be used to produce an RLSE representation from a rendered digital image.

While the best results are achieved with digital images that are in a relative log scene exposure representation, the present invention can be used to enhance the appearance of digital images that are in other representations such as linear and gamma domain representations described above. In particular, the present invention can be used to process digital images that are in a visual domain such as CIELAB (one of the color spaces defined by the International Commission on Illumination abbreviated as CIE).

The scene balance module 320 receives the RLSE digital image from the RLSE conversion module 310 and performs a lightness and color balance adjustment. The resulting processed digital image is called the source digital image 102 since it is the input digital image to the tone scale module 330. The lightness adjustment to the pixel data accounts for exposure variability in the original digital image 101. The present invention can be used with any algorithm that performs a lightness adjustment, or luminance balance, of the input pixel data. In particular, the lightness balance procedure includes calculating one or more prediction features from the RLSE digital image. These predication features are combined using a linear equation of the prediction features into a single brightness balance value that relates to an estimate of the pixel value corresponding to a theoretical 18% gray scene reflector. While there are many such prediction features that are useful, the present invention uses a spatial activity measure as the most important prediction feature. An RLSE luminance-chrominance digital image is generated from the RLSE digital image. Next, a spatial activity filter is applied to the luminance pixel data of the RLSE luminance-chrominance digital image. The spatial activity filter calculates the minimum difference of adjacent pixel values within a 3 by 3 pixel region and assigns the minimum difference to the pixel. Pixels with minimum difference values that exceed a predetermined threshold are averaged to produce the first prediction feature. The second prediction feature is calculated by dividing the luminance pixels of the RLSE luminance-chrominance digital image into four strips and calculating the average the maximum pixel value from each of the four strips. Other prediction features that have proved to be useful are the average pixel value and the 90% cumulative histogram pixel value.

After the brightness balance value of the RLSE digital image has been calculated, the color cast of the RLSE digital image is calculated that can be used to remove an overall color cast. The principle causes of color casts are variation in the color of the source illumination and secondarily the color fidelity of the image sensor that recorded the original digital image. A color balance position is calculated for the RLSE digital image which represents the chrominance coordinates of a theoretical color-neutral scene reflector. The color balance position is calculated using a two dimensional Gaussian weighting surface applied to the chrominance pixel data of the RLSE luminance-chrominance digital image. Although the chrominance pixel data can be averaged in an unweighted manner, better results have been obtained using the two dimensional Gaussian weighting surface. This is principally due to the de-emphasis of highly colorful scene objects from the calculation. A balance look-up-table is calculated for each color of the RLSE digital image using the calculated color balance position and the brightness balance value. The balance look-up-table is applied to the RLSE digital image to produce the source digital image for the tone scale module 330. As a result of the application of the balance look-up-table, pixel values in the RLSE digital image that have a value equal to the predicted balance value are transformed to a value equal to a system defined reference gray point. Similarly, pixels with corresponding chrominance values that correspond to the color balance position will be transformed to a color neutral position.

It should be noted that it is also possible to combine the operation of the lightness balance procedure described above with the construction of the tone scale function described in later detail below. The lightness balance operation is equivalent to adjusting the reference gray point used in the construction of the tone scale function. If the lightness balance operation is combined with the construction of the tone scale function, the reference gray point is image dependent since the pixels of the digital image being processed are used to calculate the reference gray point.

The rendering module 340 accepts the enhanced digital image from the tone scale module 330 and prepares the pixel data for display on an output image device. The rendering module 340 performs a color transformation, a tonal rendering transformation, and an output encoding transformation. The pixel data must be transformed such that the pixel data representing different original scene colors are appropriate for the color spectral characteristics associated with the output image device. This can be accomplished in a multiple step procedure. First the RLSE representation pixel data of the enhanced digital image is transformed into a linear representation. Next a color matrix transform is applied to the linear representation pixel data. The color matrix can be a 3 by 3 element matrix wherein the elements of the matrix are determined by analyzing a collection of imaged color patch targets and measuring the resultant color produced with the intended output image device.

The other task includes the transformation of the pixel data from the linear or relative-log-scene-exposure representation to a rendered representation with the application of a rendering function R(x). In general, the dynamic range of the enhanced digital image is much larger than can be displayed on typical output image devices such as CRT monitors or photographic paper. Therefore, if the pixel data of the enhanced digital image were received by the output image device directly, much of the pixel data would be clipped in the extreme light and dark parts of the image with a substantial loss of spatial detail. The rendering function performs a graceful roll-off of the pixel data such that the processed pixel data when displayed will result in a gradual loss of spatial detail as pixel values approach the limits of the output image device.

The present invention can be used with many different mathematical forms for the rendering function such as a rendering function produced with the method disclosed in commonly-assigned U.S. Pat. No. 5,300,381 by Buhr et al. In general, optimal results are obtained using rendering functions that have a sigmoid shape, i.e., rendering functions having a zero or near slope for extreme input pixel values and having a maximum magnitude slope that is achieved for mid-tone input values. Some rendering functions can have a relatively high slope for the darkest domain of input pixel values but almost all rendering functions have a zero or near zero slope characteristic for the lightest domain of pixel values. The rendering function, to an extent, mimics the photo response characteristics of photographic paper used in analog imaging applications.

Although it is possible to use a rendering function that is dependent on the digital image being processed, the preferred embodiment of the present invention uses a sigmoid shaped rendering function that is independent of the digital image. Image dependent modification to the digital image being processed is performed by the tone scale module 330 prior to the application of the rendering function. Thus, the tone scale module 330 is responsible for scene dependent changes to the image content while the rendering module 340 is responsible for preparing the final processed image for an image output device.

FIG. 3 a shows a graph of an example rendering function R(x) (indicated by curve 590) that is suitable for use with the present invention. Point 591 corresponds to an input pixel value equal to the reference gray point. Point 592 indicates the rendering function response for a highlight pixel value corresponding to a light region in the original digital image 101. Point 593 indicates the rendering function response for a shadow pixel value corresponding to a dark region in the original digital image 101. Point 595 indicates the rendering function response corresponding to brightest reproduced output value of the output image device. Similarly, point 596 indicates the rendering function response corresponding to the darkest reproduced output value of the output image device. Point 597 indicates the point on the rendering function for which the instantaneous slope has a maximum magnitude which does not necessarily coincide with the reference gray point 591. The point of maximum magnitude slope is also the inflection point of the sigmoid function, i.e. a local maximum or minimum in the corresponding slope function of the rendering function R(x). The example rendering function shown in FIG. 3 a is appropriate for a digital imaging system that uses a 12-bit pixel value representation for processing digital images. Many output display devices accept digital images with an 8-bit pixel value representation. The example rendering function shown in FIG. 3 a maps 12-bit input pixel values ranging from 0 to 4095 to output rendering pixel values ranging from 255 to 0.

The example rendering function shown in FIG. 3 a is typical for use with a digital printer that produces digital photographic prints. The output pixel value scale shown can relate to photographic paper densities, e.g. the digital printer can be calibrated such that each pixel code value is equal to 100 times the optical density that will be produced on the print. However, the present invention can also be used with electronic display devices. A typical rendering function for use with an electronic display device is shown in FIG. 3 b as indicated by curve 598. Note that the rendering function R(x) shown in FIG. 3 b produces higher numerical output rendering pixel values for higher numerical input pixel values. For a typical computer monitor device, the output pixel values will range from 0 (representing black) to 255 (representing white). The example rendering function shown in FIG. 3 b maps 12-bit input pixel values ranging from 0 to 4095 to output rendering pixel values ranging from 0 to 255.

The last operation performed by the rendering module 340 is the encoding of the output pixel values for use with an output image device. Most output image devices are calibrated to accept pixel data with a known implied relationship. For example, some digital image printers are calibrated to accept visual lightness related pixel data while other devices are calibrated for optical density related pixel data. The encoding operation performs an intensity transformation that prepares the pixel data for the specific device.

The rendering module 340 can also perform the operation of preparing the image pixel data of the enhanced digital image for an unspecified output image device. For example, the pixel data can be transformed into CIEXYZ coordinates such as defined by the International Image Consortium's Profile Connection Space.

Referring to FIG. 2, the chain of image processing modules is designed to work well with or without the inclusion of the tone scale module 330. The scene balance module 320 accommodates exposure and illumination system variability. The rendering module 340 prepares the image data for viewing on an output image device. It will be appreciated by those skilled in the art that the digital image processor 20 as depicted in FIG. 2 without the use of the tone scale module 330 will yield acceptable photographic results. That is, the contrast, lightness, and color reproduction of the system are set for optimal photographic results for the majority of typical digital images. The inclusion of the tone scale module 330 enhances the appearance of the processed digital images such that digital images that deviate from the norm, in terms of dynamic range, will be tonally enhanced and digital images that are at the norm for the system will be unaltered by the tone scale module 330. For a typical implementation of the present invention, the dynamic range of the original digital images 101 (ratio of corresponding original scene intensities based on the extremes of the image) is approximately 64 to 1. The enhanced digital images corresponding to processed original digital images 101 that have approximately a 64 to 1 dynamic range will be little affected by the tone scale module 330. Conversely, the enhanced digital images corresponding to processed original digital images 101 that have a dynamic range greater than 64 to 1 and less than 64 to 1 can be significantly enhanced by the tone scale module 330, i.e. rendered with more detail and more color saturation. In particular, low contrast original digital image will, in general, experience a contrast gain, while high contrast original digital images will, in general, experience a reduction in contrast resulting in more spatial detail and more saturated color.

The tone scale module 330 depicted in FIG. 2 is illustrated in more detail in FIG. 4. The source digital image 102 is received by the analysis image generator 250, which produces a lower spatial resolution digital image from source digital image 102 called the analysis digital image 201. The tone scale function generator 230 receives the analysis digital image 201, analyzes the content of the analysis digital image 201, and produces the tone scale function 203. The tone scale function 203 is a single valued function that is defined for the range of pixel values in the source digital image 102. The tone scale function applicator 240 applies the tone scale function 203 to the source digital image 102 to generate the enhanced digital image 104. The shape of the tone scale function 203 determines the contrast and lightness changes that will be imparted to the enhanced digital image 104. In this embodiment, the tone scale function applicator 240 applies the tone scale function 203 to the individual color digital image channels of the source digital image 102.

The present invention can also be practiced with an original digital image 101 that is in a luminance-chrominance representation. FIG. 5 shows the preferred embodiment of the present invention that applies the tone scale function 203 to a luminance digital image 107 derived from the source digital image 102. The LCC conversion module 210 receives the source digital image 102 which includes red, green, and blue pixels, and produces the luminance digital image 107 containing a single digital image channel and a chrominance digital image 109 containing two chrominance, or color difference, digital image channels. It is also possible that the image input devices 10 a, 10 b, and 10 c produced the original digital image 101 in a luminance-chrominance representation. If such is the case, the LCC conversion module 210 is not employed. The analysis image generator 250 receives the source digital image 102 and generates the analysis digital image 210 as described above. Similarly, the tone scale function generator 230 receives the analysis digital image 201 and generates the tone scale function 203. The tone scale function applicator 240 receives the tone scale function 203 and applies it to the luminance digital image 107 which results in the enhanced luminance digital image 113. An RGB conversion module 220 combines the enhanced luminance digital image 113 and the chrominance digital image 109 to produce the enhanced digital image 104.

The LCC module 210 shown in FIG. 5 employs a 3 by 3 element matrix transformation to convert the red, green, and blue pixel values of the source digital image 102 into luminance and chrominance pixel values. Let the variables R_(ij), G_(ij), and B_(ij) refer to the pixel values corresponding to the red, green, and blue digital image channels located at the i^(th) row and j^(th) column. Let the variables L_(ij), GM_(ij), and ILL_(ij) refer to the transformed luminance, first chrominance, and second chrominance pixel values respectively of an LCC representation digital image. The 3 by 3 elements of the matrix transformation are described by (1). L _(ij)=0.333 R _(ij)+0.333 G _(ij)+0.333 B _(ij) GM _(ij)=−0.25 R _(ij)+0.50 G _(ij)−0.25 B _(ij) ILL _(ij)=−0.50 R _(ij)+0.50 B _(ij)  (1) Those skilled in the art will recognize that the exact values used for coefficients in the luminance/chrominance matrix transformation may be altered and still yield substantially the same effect. An alternative also used in the art is described by (2). L _(ij)=0.375 R _(ij)+0.500 G _(ij)+0.125 B _(ij) GM _(ij)=−0.250 R _(ij)+0.500 G _(ij)−0.250 B _(ij) ILL _(ij)=−0.500 R _(ij)+0.50 B _(ij)  (2)

The RGB conversion module 220 shown in FIG. 5 employs a 3 by 3 element matrix transformation to convert the luminance and chrominance pixel values into red, green, and blue pixel values by performing the inverse matrix operation to the LCC conversion module 210. The matrix elements of the RGB conversion module are given by (3) and represents the inverse matrix of the matrix given by (1). R _(ij) =L _(ij)−0.666 GM _(ij) −ILL _(ij) G _(ij) =L _(ij)+1.333 GM _(ij) B _(ij) =L _(ij)−0.666 GM _(ij) +ILL _(ij)  (3)

The tone scale function applicator 240 shown in FIG. 4 and FIG. 5 is shown in more detail in FIG. 6. The pedestal generation module 410 receives the digital image to be processed denoted by the input digital image 401 and produces the pedestal digital image 403. The pedestal generation module 410 employs a spatial filter, applied to the input digital image 401, that removes texture from the input digital image 401 but leaves edge information. In the context of the present invention, texture refers to image content including fine spatial detail and spatial shading structure. As a natural consequence of the filter, noise is also removed. Thus the pedestal digital image 403 is a highly smoothed version of the input digital image 401 with much of the edge content of the input digital image 401. The pedestal digital image 403 is subtracted from the input digital image 401 by the difference module 420 resulting in the texture digital image 402. It will be appreciated that all of the information of the input digital image 401 is contained in either the pedestal digital image 403 or the texture digital image 402. The tone scale pedestal applicator 430 applies the tone scale function 203 to the pedestal digital image 403 resulting in the tone adjusted digital image 407. Therefore, the tonal characteristics of the pedestal digital image 403 are enhanced by the application of the tone scale function 203 without affecting the texture content of the input digital image 401. The addition module 440 combines the tone adjusted digital image 407 with the texture digital image 402 to produce the output digital image 409. Therefore, the output digital image 409 is a tone scale enhanced version of the input digital image 401 that has the texture image structure content of the input digital image 401.

Applying a tone scale function by using a spatial filter is particularly advantageous when the tone scale function is highly compressive. When highly compressive tone scale functions are applied directly to an input digital image, the tonal characteristics will be enhanced but the texture image structure can be diminished in magnitude. Using a spatial filter to apply the tone scale function can achieve the desired tone scale enhancement while maintaining the texture image structure. A variety of spatial filters can be used to achieve an improved result. The present invention uses the spatial filter disclosed in commonly-assigned U.S. Pat. No. 6,317,521 by Gallagher and Gindele which employs the use of a control, or masking, signal to preserve the edges in the pedestal digital image 403. The present invention can also be used with a similar method disclosed by Lee in commonly-assigned U.S. Pat. No. 6,285,798. Another spatial filter that can be employed is a simple low-pass filter, such as a two dimensional Gaussian filter. However, if a low-pass filter is used, the dimension of the Gaussian standard deviation parameter should be very large, e.g. one quarter of the dimension of the digital image being processed.

It should also be noted that for mildly compressive tone scale functions it may not be necessary to use a spatial filter. Thus the tone scale function applicator 240 can apply the tone scale function 203 directly to the pixels of the input digital image 401 to produce the output digital image 409. It should also be noted that for expansive tone scale functions, the direct application of the tone scale function 203 to the input digital image 401 can result in a greater level of tonal enhancement than if a spatial filter is used as described above. This is principally due to the amplification of image texture resulting from the expansive tone scale function. However, for some digital images the amplification of image texture can be a detriment to image quality if the image texture also contains high levels of noise. Therefore, applying the tone scale function 203 with a spatial filter as described above can be an advantage for both compressive and expansive tone scale functions.

The tone scale function generator 230 shown in FIG. 4 and FIG. 5 is described in more detail hereinbelow. The tone scale function generator 230 can be used in either an automatic mode, wherein the tone scale function 203 is calculated using the pixels of the analysis digital image 201, or in a manual mode, wherein the tone scale function 203 is calculated using user input selections 231 provided via a graphical user interface. For either mode, the mathematical formulation for the calculations is the same.

The tone scale function 203, is a single valued function, i.e. one output pixel value for each input pixel value, defined for the range of pixels values in the source digital image 102. The shape of the tone scale function 203 is an important aspect of the present invention since the mathematical shape determines the effect on the processed digital images. The present invention constructs the tone scale function 203 from at least two function segments wherein no two function segments share more than one input pixel value in common. The preferred embodiment of the present invention uses two function segments and defines a reference gray point pixel value corresponding to an 18% scene reflector as the input pixel value in common that divides the function domain into the two tone scale segments. The function segment relating to the brighter image regions, i.e. image regions corresponding to bright regions of the original photographed scene, is called the highlight tone scale segment. The function segment relating to the darker image regions, i.e. image regions corresponding to dim regions of the original photographed scene, is called the shadow tone scale segment. It should be noted that the tone scale function 203 is a continuous function insofar as the implementation in computer software and/or hardware will allow. It should also be noted that the tone scale function 203 can have a continuous first derivative. However, although desirable, the property of a continuous first derivative is not a requirement of the present invention.

The pixel polarity of a digital image used in a digital imaging system is an arbitrary decision made by the system architect. For example, positive pixel polarity digital images have pixels wherein higher numerical values relate to more light having been received. Conversely, negative pixel polarity digital images have pixels wherein higher numerical values relate to less light having been received. The present invention can be used with digital images of either pixel polarity. However, in the interest of clarity, the following description will assume a positive pixel polarity convention. Those skilled in the art will recognize that references made to the increasing or decreasing function slope values are with respect to positive pixel polarity digital images. The description of function slope characteristics must be reversed for systems using negative pixel polarity digital images. This is an important aspect of interpretation since mathematically an increasing or decreasing function is defined with respect to numerically increasing abscissa values. For example, in the description of the construction of the highlight and shadow component functions given hereinbelow, some shadow component functions are described as having a monotonically increasing slope property while some highlight component functions are described as having a monotonically decreasing slope property. This description is with regard to a positive pixel polarity convention. For a negative pixel polarity convention, the equivalent shadow component functions would be described as having a monotonically decreasing slope property while the equivalent highlight component functions would be described as having a monotonically increasing slope property. Similarly, for a positive pixel polarity convention, the tone scale function has a slope function that is always greater than or equal to zero. Conversely, or a negative pixel polarity convention, the tone scale function can have a slope function that is always less than or equal to zero.

The highlight and shadow tone scale segments can each be constructed using two or more component functions. The combined effect of multiple component functions results in a tone scale segment that has at least one inflection point within the range of input pixel values. An inflection point can best be understood by examining the slope function of a tone scale segment. For an inflection point to occur, the corresponding slope function must achieve a local minimum or maximum. Both the highlight and shadow tone scale segments can achieve an inflection. It is also possible for one of the tone scale segments to achieve an inflection point while the other tone scale segment does not. As will be described in more detail hereinbelow, it is also possible for one tone scale segment to achieve more than one inflection point.

The highlight tone scale segment can be constructed from one or more component functions some of which satisfy the following constraints: 1) the component function can have a monotonically decreasing slope function for all input pixel values equal to or greater than the reference gray point, and 2) the component function can have a monotonically increasing function value for all input pixel values equal to or greater than the reference gray point and less than or equal to a maximum input pixel value expressed in the digital image. A function is monotonic over a given domain if the function does not have a reversal of its first derivative function (for digital implementations the slope function is taken as a reasonable approximation of the first derivative function). It should also be noted that the function characteristics for input pixel values greater than what is expressed in a particular image is an academic issue since no pixels will be affected. Both of the above mentioned constraints are important and require some explanation.

The highlight tone scale segment relates to the bright pixels, i.e. pixels relating to more light having been received. In general, for high dynamic range digital images the corresponding rendered digital images produced without the present invention have little or no spatial detail in the very brightest image regions. This is a consequence of the overall high system contrast required to pleasingly render digital images of average dynamic range. Therefore, for high dynamic range digital images some of the image content contained in the bright pixels cannot be rendered such that spatial detail modulation is preserved in the rendered digital image 103. Improved spatial detail modulation can be achieved if the tone scale function 203 maps high input pixel values to lower output pixel values. This results in processed digital images with darker highlight content in the resultant rendered digital image 103. There are many functions that can perform such an input to output mapping operation. However, monotonically increasing functions have been experimentally determined to be more robust, i.e. produce fewer image artifacts, than functions that are not monotonic.

While many monotonic functions can achieve the operation of mapping high input pixel values to lower output pixel values, all functions impose some form of compromise in contrast with regard to image regions corresponding to different average pixel values. In particular, the instantaneous slope value (first derivative) of the component functions used to construct the highlight tone scale segment can significantly affect the perception of contrast and spatial detail modulation in the resultant rendered digital image 103. Therefore, the highlight tone scale segment constructed using component functions having a monotonically decreasing instantaneous slope value can improve the rendering of spatial detail modulation for bright image regions by mapping highlight pixels to lower output pixel values. Image regions corresponding to higher instantaneous slope values within the domain of the highlight tone scale segment tend to preserve more image detail modulation. Thus the monotonically decreasing instantaneous slope condition advantages image content corresponding to pixel values that are numerically closer in value to the reference gray point. In general, important image content, such as the main subject region, tends to be numerically closer to the reference gray point while background image content tends to be exhibited more uniformly with regard to pixel values.

Similarly, the shadow tone scale segment can be constructed from one or more component functions some of which satisfy the following constraints: 1) the component function can have a monotonically increasing slope function for all input pixel values equal to or less than the reference gray point, and 2) the component function can have a monotonically increasing function value for all input pixel values equal to or less than the reference gray point and greater than or equal to a minimum input pixel value expressed in the digital image. Similarly, the monotonicity property of the component functions used to construct the shadow tone scale segment relates to more robust image quality results. The monotonically increasing slope function property of the component functions used to construct the shadow tone scale segment is similarly important since this condition also advantages image content corresponding to pixel values that are numerically closer in value to the reference gray point. For high dynamic range images, the monotonically increasing slope function property of the component functions used to construct the shadow tone scale segment achieves a low input pixel value to higher output pixel value mapping operation. This results in processed digital images with lighter shadow content in the resultant rendered digital image 103.

A natural consequence of the above mentioned slope function constraints produces tone scale functions that have high slope function values at the reference gray point. Therefore the choice of the reference gray point value is important since it determines which regions in images will experience high slope function values. A reference gray point value corresponding to an 18% gray scene reflector is chosen since it represents approximately the midpoint of perceptual lightness. Other choices for the value of the reference gray point can also produce excellent results. Reasonable values for the reference gray point range from a 10% scene reflector value to a 25% scene reflector value.

In a first embodiment of the tone scale function generator 230 shown in FIG. 4 and FIG. 5, either the highlight or the shadow tone scale segment is constructed using a single component function based on exponential functions. The component function used for the highlight tone scale segment is given by the formula (4) ƒ_(h1)(x)=β_(h1)(1−e ^(−(x−x) ^(ρ) ^()/α) ^(h1) )+x_(ρ)  (4) where x_(ρ) represents the reference gray point, and β_(h1) and α_(h1) are numerical constants that determine the shape and slope of the component function ƒ_(h1)(x). The component function used for the shadow tone scale segment is given by the formula (5) ƒ_(s1)(x)=β_(s1)(1−e ^(−(x−x) ^(ρ) ^()/α) ^(s1) )+x _(ρ)  (5) where β_(s1) and α_(s1) are numerical constants that similarly determine the shape and slope of the component function ƒ_(s1)(x). If a slope constraint of 1.0 is imposed at the reference gray point, the constants β_(h1) and β_(s1) are equal to α_(h1) and α_(s1) respectively. For this condition, the equations for the functions ƒ_(h1)(x) and ƒ_(s1)(x) are given as (6) and (7) ƒ_(h1)(x)=α_(h1)(1−e ^(−(x−x) ^(ρ) ^()/α) ^(h1) )+x _(ρ)  (6) ƒ_(s1)(x)=α_(s1)(1−e ^(−(x−x) ^(ρ) ^()/α) ^(s1) )+x _(ρ)  (7) and the expression for the tone scale function 203 T(x) is given by (8). T(x)=ƒ_(h1)(x) for x>=x _(ρ) T(x)=ƒ_(s1)(x) for x<x _(ρ)  (8)

The highlight component function is constrained to pass through a specified coordinate point defined by an abscissa value x_(ho) that results in an ordinate value x_(w) as given by (9). x _(w)=α_(h1)(1−e ^(−(x) ^(ho) ^(−x) ^(ρ) ^()/α) ^(h1) )+x _(ρ)  (9) This constraint achieves a highlight white point mapping objective. For the highlight component function, the white point value x_(w) is predetermined based on the pixel value that is preferably mapped by the rendering function R(x) to correspond to a photographic white paper density of approximately 0.2. With the variables x_(w) and x_(ρ) defined, the value of the variable α_(h1) can be solved for a given value of x_(ho) using expression (9) by an iterative numerical solution. Similarly, the shadow component function is constrained to pass through a specified coordinate point defined by an abscissa value x_(so) that results in an ordinate value x_(b) as given by (10). x _(b)=α_(s1)(1−e ^(−(x) ^(so) ^(−x) ^(ρ) ^()/α) ^(s1) )+x _(ρ)  (10) This constraint achieves a shadow black point mapping objective. For the shadow tone scale function, the black point value x_(w) is predetermined based on the pixel value that is preferably mapped by the rendering function R(x) to correspond to a photographic black paper density of approximately 2.0. Generally speaking, the aforementioned white point value and dark point value represent first and second predetermined target densities or code values. While these target densities have been described with reference to density measurements of photographic paper, those skilled in the art will recognize that the same approach may be used even if the output device is not photographic paper (a computer monitor, for example.) With the variables x_(b) and x_(ρ) defined, the value of the variable α_(s1) can be solved for a given value of x_(so) using expression (10) by the iterative numerical solution. The iterative numerical solution for expressions (9) and (10) includes a process of first estimating an initial value of α_(s1), calculating each side of the equation, calculating an error term as the difference, inspecting the error, making an adjustment to the estimate of α_(s1), and iterating the procedure until the error term is of an acceptably low magnitude. The iterative solution results are computed for all possible values of α_(s1) and stored in a LUT. The same calculations and procedure are used to determine the value of α_(h1).

The variables x_(ho) and x_(so) are control variables in the expressions (9) and (10) that, once selected, determine the function shape and slope characteristics for expressions (6) and (7). FIG. 7 shows a graph depicting a family of curves that represent highlight tone scale segments generated with different values of x_(ho) and a family of curves that represent shadow tone scale segments generated with different values of x_(so). Point 500 represents the reference gray point x_(ρ). Curve 501 represents a highlight tone scale segment constructed from one highlight component function. Curve 502 represents a shadow tone scale segment constructed from one shadow component function. Line 503 represents the one-to-one input-to-output line. For each of the highlight tone scale segments shown in FIG. 7, the highlight component function graphed has a monotonically decreasing instantaneous slope value. Similarly, for each of the shadow tone scale segments shown in FIG. 7, the component function graphed has a monotonically increasing instantaneous slope value. The variables x_(ho) and x_(so) can be selected independently. Thus the shape of the two tone scale segments can be controlled independent from one another.

The expressions (6) and (7) were derived with the constraint that the slope function (corresponding to the component function), when evaluated at the reference gray point, must be equal to 1.0. In another embodiment, exponential functions are used in similar fashion with a slope constraint imposed. The slope of the highlight component function must be equal to a selected value φ_(h) and the shadow component function must be equal to a selected value φ_(s). For this embodiment, the imposed slope constraint results in a relationship between the variables β_(h1) and α_(h1) in expression (4) and β_(s1) and α_(s1) in expression (5) given by expressions (11) and (12) respectively. β_(h1)=φ_(h)α_(h1)  (11) β_(s1)=φ_(s)α_(s1)  (12) The expressions for the highlight component function and the shadow component function are given by expressions (13) and (14) respectively. ƒ_(h1)(x)=φ_(h)α_(h1)(1−e ^(−(x−x) ^(ρ) ^()/α) ^(h1) )+x _(ρ)  (13) ƒ_(s1)(x)=φ_(s)α_(s1)(1−e ^(−(x−x) ^(ρ) ^()/α) ^(s1) )+x _(ρ)  (14)

The first derivative functions that represent the slope function of the highlight and shadow component functions are given by expressions (15) and (16) respectively. ƒ_(h1)′(x)=φ_(h) e ^(−(x−x) ^(ρ) ^()/α) ^(h1)   (15) ƒ_(s1)′(x)=φ_(s) e ^(−(x−x) ^(ρ) ^()/α) ^(s1)   (16)

If the value of x_(ho) is greater than the value of x_(w), the highlight component function will map a greater range of input pixel values to a lesser range of output pixel values and is therefore considered a compressive function. Conversely, if the value of x_(ho) is less than the value of x_(w), the highlight component function will map a lesser range of pixel values to a greater range of pixel values and is therefore considered an expansive function. Similarly, if the value of x_(so) is less than the value of x_(b), the shadow component function will map a greater range of input pixel values to a lesser range of output pixels value and is therefore considered a compressive function. Conversely, if the value of x_(so) is greater than the value of x_(b), the shadow component function will map a lesser range of pixel values to a greater range of pixel values and is therefore considered an expansive function. Therefore, based on the values of the variables x_(ho) and x_(w), the highlight tone scale segment can be classified as compressive, expansive, or neutral. When the value x_(ho) is equal to the value of x_(w), the highlight tone scale segment is classified as neutral since for this unique condition the highlight tone scale segment assumes the identity mapping function. Based on the values of the variables x_(so) and x_(b), the shadow tone scale segment can be classified as compressive, expansive, or neutral. Similarly, when the value x_(so) is equal to the value of x_(b), the shadow tone scale segment is classified as neutral since for this unique condition the shadow tone scale segment assumes the identity mapping function.

For compressive highlight component functions, the numerical constant α_(h1) is positive. The corresponding slope function of the highlight component function given by expression (15) for positive values of φ_(h) yields positive slope function values for all x values greater than or equal to the reference gray point x_(ρ). The expression for the second derivative function, or the slope function of the slope function of the highlight component function is given by expression (17). ƒ_(h1)″(x)=−(φ_(h)/α_(h1))e ^(−(x−x) ^(ρ) ^()/α) ^(h1)   (17) FIG. 8 a shows an example graph of a highlight tone scale segment generated with a φ_(h) variable set to 1.0, a reference gray point set to 0.0 and α_(h1) variable set to 1.0. As can be seen by inspection of the graph shown in FIG. 8 a and by expression (17), positive values of α_(h1) and φ_(h) result in a compressive highlight tone scale segment with a monotonically decreasing slope function that assumes values that are greater than or equal to zero. Curve 512 depicts such a highlight component function used to construct a highlight tone scale segment and curve 513 depicts its corresponding slope function. The reference gray point is indicated by point 511.

Similarly, for compressive shadow component functions, the numerical constant α_(s1) is negative. The corresponding slope function of the shadow component function given by expression (16) for the positive values of φ_(s) yields positive slope values for all x values less than or equal to the reference gray point x_(ρ). The expression for the second derivative function, or slope function of the slope function of the shadow component function is given by expression (18). ƒ_(s1)″(x)=−(φ_(s)/α_(s1))e ^(−(x−x) ^(ρ) ^()/α) ^(s1)   (18) FIG. 8 b shows an example graph of a shadow tone scale segment generated with a φ_(s) variable set to 1.0, a reference gray point set to 0.0 and a α_(s1) variable set to 1.0. As can be seen by inspection of the graph shown in FIG. 8 b and by expression (18), positive values of α_(s1) and φ_(s) result in a compressive shadow tone scale segment with a monotonically increasing slope function that assumes values that are greater than or equal to zero. Curve 515 depicts such a shadow component function used to construct a shadow tone scale segment and curve 516 depicts its corresponding slope function. The reference gray point is indicated by point 514.

An example tone scale function 203, shown in FIG. 8 c, was constructed from a highlight and shadow tone scale segment, each of which is constructed from a single component function as described above. The equal slope condition, as described above, is not a requirement of the tone scale functions generated by the present invention. In the example tone scale function shown in FIG. 8 c, the slopes of the highlight and shadow tone scale segments are not equal when evaluated at the reference gray point 519. Curve 517 represents the tone scale function 203 and curve 518 its corresponding slope function. The reference gray point is indicated by point 519. As can be seen in the example graph shown in FIG. 8 c, the tone scale function has a discontinuity in the slope function at the reference gray point. Tone scale functions having continuous slope function are, in general, desirable. However, experimentation has shown that the discontinuity in the slope function of a tone scale function when applied to digital images relating to natural photographed scenes is not often a problem. Other methods for generating tone scale functions, such as described in commonly-assigned U.S. Pat. No. 6,285,798 have imposed a continuity of slope constraint on the process of construction. The experimentation performed in support of the present invention has found that the continuity of slope constraint can be unnecessarily restrictive for some digital imaging applications. By not imposing the continuity of slope constraint, a greater diversity of useful tone scale functions can be produced. In particular, tone scale functions that are constructed in a manner responsive to the pixels of the source digital image when applied to the source digital image can achieve a greater level of overall contrast enhancement.

In another embodiment, the highlight tone scale segment is constructed from a compressive highlight component function. Recall that for compressive highlight component functions x_(ho) is greater than x_(w). For this embodiment, the expression given by (6) is combined with a linear function which relaxes the function's slope condition at the reference gray point. The expression for the highlight component function is given by (19) ƒ_(h1)(x)=(1−φ_(HC))α_(h1)(1−e ^(−(x−x) ^(ρ) ^()/α) ^(h1) )+φ_(HC)γ_(HC)(x−x _(ρ))+x _(ρ)  (19) where the variable γ_(HC) represents the average slope for the function over the interval from x_(ρ) to x_(ho) and is given by expression (20). γ_(HC)=(x _(w) −x _(ρ))/(x _(ho) −x _(ρ))  (20) The variable φ_(HC) determines the contribution of the linear function to the highlight component function. The variable φ_(HC) can be selected to affect a change in the shape of the highlight tone scale segments that uses expression (19) as a highlight component function. If φ_(HC) is set to 0.0, expression (19) reverts to expression (6). If φ_(HC) is set to 1.0, the expression (19) assumes a linear function given by expression (21). ƒ_(h1)(x)=γ_(HC)(x−x _(ρ))+x _(ρ)  (21) Thus the variable φ_(HC) is a control parameter that can be used to select the degree to which the highlight component function behaves as a pure exponential function. Similarly, the expression for the shadow component function is given by (22) ƒ_(s1)(x)=(1−φ_(SC))α_(s1)(1−e ^(−(x−x) ^(ρ) ^()/α) ^(s1) )+φ_(SC)γ_(SC)(x−x _(ρ))+x _(ρ)  (22) where the variable γ_(SC) represents the average slope for the function over the interval from x_(so) to x_(ρ) and is given by expression (23). γ_(SC)=(x _(b) −x _(ρ))/(x _(so) −x _(ρ))  (23) The variable φ_(SC) can be selected to change the shape of the shadow tone scale segments that use expression (22) as a shadow component function. If φ_(SC) is set to 0.0, expression (22) reverts to expression (7). If φ_(SC) is set to 1.0, expression (22) assumes a linear function given by expression (24). ƒ_(s1)(x)=γ_(SC)(x−x _(ρ))+x _(ρ)  (24) Thus the variable φ_(SC) is a control parameter that can be used to select the degree to which the shadow component function behaves as a pure exponential function.

Changing the φ_(HC) and φ_(SC) variables can have a significant impact on the appearance of the processed digital images. Setting the φ_(HC) and φ_(SC) variables toward 0.0 results in processed digital images that have a more traditional photographic high contrast appearance. Conversely, setting the φ_(HC) and φ_(SC) variables toward 1.0 results in processed digital images that have a more professional photographic low contrast appearance more appropriate for portraiture. FIG. 9 shows a graph depicting a family of curves that represent highlight tone scale segments generated with expression (19) using different values of x_(ho) and a family of curves that represent shadow tone scale segments generated with expression (22) using different values of x_(so). Point 521 represents the reference gray point x_(ρ). Curve 522 represents a highlight tone scale segment constructed from one highlight component function. Curve 523 represents a shadow tone scale segment constructed from one shadow component function. Line 524 represent the identity mapping one-to-one input pixel value-to-output pixel value line. The tone scale function 203 can be constructed from any of the highlight tone scale segments depicted in FIG. 9 or any of the shadow tone scale segments since the variables x_(ho) and x_(so) can be selected independently. It should also be noted that if the variables φ_(HC) and φ_(SC) are not set to 0.0, the resultant tone scale function will, in general, not have a continuous slope function at the reference gray point.

A highlight component function constructed with expressions (4), (6), or (13), for the case in which x_(ho) is less than x_(w), will result in a function that has a montonically increasing instantaneous slope. An example of such a function is depicted as curve 530 in FIG. 10 a. While the function indicated by curve 530 satisfies the mapping of input pixel value x_(ho) to output pixel value x_(w), a tone scale function based on such a function can produce some unnatural looking images. This is mainly due to the fact that the slope of the highlight component function depicted by curve 530 has a slope that is monotonically increasing, not monotonically decreasing, for the input pixel range near the reference gray point. However, a highlight component function can be constructed using the expressions (4), (6), or (13), reflected about the line indicated as line 531 given by (25) for input pixel values greater than or equal to the reference gray point (indicated by point 533). y(x)=(x _(w) −x _(ρ))/(x _(ho) −x _(ρ))(x−x _(ρ))+x _(ρ)  (25) The function produced by the reflection process, as indicated by curve 532 in FIG. 10 a, is a monontonically increasing function with a monotonically decreasing instantaneous slope. The functional form of the expansive highlight component function is calculated by the following steps. First the expression (4), (6), or (13) is solved using the constraint x_(ho)=x_(w) to determine the numerical constant α_(h1). In the second step, a first rotation transform is applied to a coordinate pair (x,ƒ(x)) resulting in a transformed coordinate pair (u,v) as given by (26) u=x cos(θ)+ƒ(x)sin(θ) v=−x sin(θ)−ƒ(x)cos(θ)  (26) where the angle θ is given by (27). θ=tan⁻¹((x _(w) −x _(ρ))/(x _(ho) −x _(ρ)))  (27) The first rotation transform is designed to transform the line described by expression (24) into the x-axis. In the third step the v coordinate is reflected about the new x-axis by taking the negative of the value v coordinate. In the fourth step, a reverse rotation transform is applied to the coordinate pair (u, −v) for the coordinate pair (u′,v′) as given by (28). u′=u cos(θ)+v sin(θ) v′=x sin(θ)−v cos(θ)  (28) In the fifth step, the coordinate pair (u′,v′) defines a highlight component function g(u) and is evaluated for the range of input pixel values.

Referring to FIG. 10 b, an expansive shadow component function can be constructed in similar manner as described above for the case in which x_(so)>x_(b). The shadow component function can be constructed using the function described by (5), (7), or (14), reflected about the line indicated as line 535 given by (29) for input pixel values less than or equal to the reference gray point (indicated by point 534) using the constraint x_(so)=x_(b) to determine the numerical constant α_(s1). y(x)=(x _(b) −x _(ρ))/(x _(so) −x _(ρ))(x−x _(ρ))+x _(ρ)  (29) The curve indicated by 536 depicted in FIG. 10 b shows a function using (5), (7), or (14) for a positive numerical constant α_(s1). The corresponding expansive shadow component function produced via the reflection processing steps is indicated by curve 537.

An example graph of a tone scale function constructed from an expansive highlight tone scale segment and an expansive shadow tone scale segment constructed with the component functions described above is shown in FIG. 10 c. Since both the highlight and shadow tone scale segments are expansive, the tone scale function, indicated by curve 539, is expansive over the range of input pixel values from x_(so) to x_(ho). The combined linear functions given by expressions (25) and (29) are shown as line 540 with the reference gray point as indicated by point 538. The tone scale function depicted in FIG. 8 c as curve 517 is compressive over the range of input pixel values from x_(so) to x_(ho) since both the highlight and shadow tone scale segments used in its construction are compressive component functions. The reference gray point is indicated by point 538.

It is possible to construct a tone scale function from an expansive shadow component function and a compressive highlight component function or from a compressive shadow component function and an expansive highlight component function since the shape of the two tone scale segments are independently controllable. Such a tone scale function is referred to herein as an eclectic function since the two segments are of different shape classification. However, as described above, the two segments must have equal function values at the one input pixel value they have in common, i.e. the reference gray point.

In a preferred embodiment, the expansive highlight component function ƒ_(h1)(x) is constructed using the expression (4) subject the constraints given by (30) and (31). ƒ_(h1)(x _(ho)′)=x _(w)  (30) ƒ_(h1)′(x _(w))=1.0  (31) where the variable x_(ho)′ is given by the expression (32) ƒ_(h1)(x _(ho)′)=(1.0−η_(H))(x _(w) −x _(ho))+x _(ho)  (32) and the variable η_(H) represents a control parameter that can be used to select the shape of the function. With these two constraints placed on the function ƒ_(h1)(x), the highlight component function achieves the goal of mapping the prescribed input pixel value x_(ho)′ to the prescribed output pixel value x_(w). The average slope of the function γ_(HE) over the interval from x_(ρ) to x_(ho)′ is given by the expression (33) γ_(HE)=(x _(w) −x _(ρ))/(x _(ho) ′−x _(ρ))  (33) which is greater than 1.0 since x_(w) is greater than x_(ho)′. In a similar manner as described above, the variables α_(h1) and β_(h1) used in the expression (4) are solved by iterative numerical approximation and stored in a LUT for later recall. The variable η_(H) is preferably set to 0.5. As a further refinement, the highlight component function is combined with a linear function using a control parameter φ_(HE). The final expression for the expansive highlight component function is given by (34). ƒ_(h1)(x)=(1−φ_(HE))α_(h1)(1−e ^(−(x−x) ^(ρ) ^()/α) ^(h1) )+φ_(HE)γ_(HE) +x _(ρ)  (34)

Similarly, the expansive shadow component function ƒ_(s1)(x) is constructed using the expression (5) subject the constraints given by (35) and (36). ƒ_(s1)(x _(so)′)=x _(b)  (35) ƒ_(s1)′(x _(b))=1.0  (36) where the variable x_(so)′ is given by the expression (37) ƒ_(s1)(x _(so)′)=(1.0−η_(s))(x _(b) −x _(so))+x _(so)  (37) and the variable η_(s) represents a control parameter that can be used to select the shape of the function. With these two constraints placed on the function ƒ_(s1)(x), the shadow component function achieves the goal of mapping the prescribed input pixel value x_(so)′ to the prescribed output pixel value x_(b). The average slope of the function γ_(SE) over the interval from x_(so)′ to x_(ρ) is given by the expression (38) γ_(SE)=(x _(ρ) −x _(b))/(x _(ρ) −x _(so)′)  (38) which is greater than 1.0 since x_(b) is less than x_(so)′. In a similar manner as described above, the variables α_(s1) and β_(s1) used in the expression (5) are solved by iterative numerical approximation and stored in a LUT for later recall. The variable η_(s) is preferably set to 0.5. As a further refinement, the shadow component function is combined with a linear function using a control variable φ_(SE). The final expression for the expansive shadow component function is given by (39). ƒ_(s1)(x)=(1−φ_(SE))α_(s1)(1−e ^(−(x−x) ^(ρ) ^()/α) ^(s1) )+φ_(SE)γ_(SE) +x _(ρ)  (39) FIG. 11 depicts a family of highlight and shadow tone scale segments each generated from a single expansive component function using expressions (34) and (39) respectively. Point 541 represents the reference gray point x_(ρ). Line 542 represents the identity mapping one-to-one input pixel value-to-output pixel value line. Curve 543 represents an example a highlight tone scale segment using expression (34). Curve 544 represents an example shadow tone scale segment using expression (39).

An important feature of the tone scale segments constructed with the method of the present invention is the gradual transition in function shape corresponding to a transition from compressive to expansive type functions. FIG. 12 a depicts a family of highlight tone scale segments constructed from a single highlight component function using expression (19) when the component function is compressive and expression (34) when the component function is expansive. Point 549 indicates the reference gray point. Curve 550 indicates a highly compressive highlight component function while curve 551 indicates a mildly highlight component function. The higher the degree of compression, the greater the curvature of the function. When the highlight component function is neither compressive nor expansive, the function assumes the identity mapping one-to-one input-to-output line indicated by curve 552. Curve 553 indicates a mildly expansive highlight component function. Curve 554 indicates a highly expansive highlight component function. The higher the degree of expansion, the greater the curvature of the function. Thus for mildly compressive and mildly expansive highlight component functions, the shape of the function is closer to a straight line. For the compressive highlight component functions used to construct the highlight component segments as shown in FIG. 12 a a value of 0.0 was used for the variable φ_(HC). For the expansive highlight component functions used to construct the highlight component segments shown in FIG. 12 a a value of 0.0 was used for the variable φ_(HE). It should be noted that the value of the φ_(HC) variable used in expression (19) can be selected independent of the value of the φ_(HE) variable used in expression (34).

The shape of the shadow tone scale segments also have a graceful transition between compressive and expansive function types. FIG. 12 a also depicts a family of shadow tone scale segments constructed from a single shadow component function using expression (22) when the component function is compressive and expression (39) when the component function is expansive. Curve 555 indicates a highly compressive shadow component function while curve 556 indicates a mildly shadow component function. The higher the degree of compression, the greater the curvature of the function. When the shadow component function is neither compressive nor expansive, the function assumes the identity mapping one-to-one input-to-output line indicated by line 557. Curve 558 indicates a mildly expansive shadow component function. Curve 559 indicates a highly expansive shadow component function. The higher the degree of expansion, the greater the curvature of the function. Thus for mildly compressive and mildly expansive shadow component functions, the shape of the function is closer to a straight line. For the compressive shadow component functions used to construct the shadow component segments as shown in FIG. 12 a a value of 0.0 was used for the variable φ_(SC). For the expansive shadow component functions used to construct the shadow component segments shown in FIG. 12 a a value of 0.0 was used for the variable φ_(SE). It should be noted that the value of the φ_(SC) variable used in expression (22) can be selected independent of the value of the φ_(SE) variable used in expression (39).

FIG. 12 b depicts a similar family of highlight and shadow tone scale segments constructed using expressions (19), (34), (22) and (39) with the values of the variables φ_(HC), φ_(HE), φ_(SC), and φ_(SE) all set to a value of 0.5. As can be seen from the curves depicted in FIG. 12 b, the tone scale segments constructed with the method of the present invention make a gradual transition in function shape for a corresponding transition from compressive to expansive type functions.

In another alternative embodiment for a compressive highlight component function, the variable φ_(h) is made a function of the degree of compression, i.e. the ratio of (x_(w)−x_(ρ)) to (x_(ho)−x_(ρ)). The expression for the highlight component function ƒ_(h1)(x) is given by (13) where the slope variable φ_(h) in expression (13) is given by (40) φ_(h)=1.0−η_(h)(1.0−(x _(w) /x _(ho)))  (40) and the variable η_(h) controls the shape of the highlight component function. The variable η_(h) can be selected. When the variable η_(h) is set to 1.0, the highlight component function assumes the equation of a line given as expression (21). When the variable η_(h) is set to 0.0, the highlight component function assumes the equation of the exponential function given as expression (13). Similarly, for a compressive shadow component function, the variable φ_(s) is made a function of the degree of compression, i.e. the ratio of (x_(ρ)−x_(b)) to (x_(ρ)−x_(so)). The expression for the shadow component function ƒ_(s1)(x) is given by (14) where the slope variable φ_(s) in expression (14) is given by (41) φ_(s)=1.0−η_(s)(1.0−(x _(b) /x _(so)))  (41) and the variable η_(s) controls the shape of the shadow component function. When the variable η_(s) is set to 1.0, the shadow component function assumes the equation of a line given as expression (24). When the variable η_(s) is set to 0.0, the shadow component function assumes the equation of the exponential function given as expression (14).

A highlight tone scale segment which achieves an inflection point can be constructed using more than one highlight component function. A second highlight component function is constructed using the same functional form as expression (6) constrained to pass through the specified coordinate point defined by an abscissa value x_(he) that results in a function value x_(we) as given by (42). x _(we)=α_(h2)(1−e ^(−(x) ^(he) ^(−x) ^(ρ) ^()/α) ^(h2) )+x _(ρ)  (42) This constraint achieves an extreme highlight white point mapping objective relating to the brightest part of the processed digital image. For the second highlight component function, the white point value x_(we) is predetermined based on the pixel value that is mapped by the rendering function R(x) to correspond to a white paper density of approximately 0.08, i.e. the minimum paper achievable paper density. The variable α_(h2) can be solved using the iterative numerical solution described above. The second highlight component function is described by expression (43). ƒ_(h2)(x)=α_(h2)(1−e ^(−(x−x) ^(ρ) ^()/α) ^(h2) )+x _(ρ)  (43) The highlight tone scale segment F_(H)(x) is constructed by combining the first highlight component function given by expression (19) with the second highlight component function given by expression (43). F_(H)(x) is given by expression (44) F _(H)(x)=ƒ_(h1)(x) for x_(ρ) <=x<=x _(hc) F _(H)(x)=ω_(h)(x)ƒ_(h1)(x)+(1−ω_(h)(x))ƒ_(h2)(x) for x _(hc) <x<=x _(he) F _(H)(x)=ƒ_(h2)(x) for x _(he) <x  (44) where the function ω_(h)(x) represents a blending function of the two component functions and is given by expression (45). ω_(h)(x)=(x−x _(hc))/(x _(he) −x _(hc))  (45) The highlight tone scale segment so constructed consists of three input pixel domains. The first domain extends from the reference gray point x_(ρ) to a point defined by the variable x_(hc). This first domain is constructed entirely from the first highlight component function ƒ_(h1)(x). The second domain is constructed using a blend of the first and second highlight component functions and extends from point x_(hc) to the extreme highlight point x_(he). The third shadow domain is constructed for the region for input pixel values greater than the value of x_(he). As input pixel values increase, denoted here by the variable x, approach x_(he), the highlight tone scale segment approaches the value of the second highlight component function ƒ_(h2)(x). Highlight tone scale segments constructed using expressions (44) can produce an inflection point in the function shape for some input pixel value x within the range from x_(ρ) to x_(he). That is, the slope of the highlight tone scale segment has a local minimum within the range from x_(ρ) to x_(he). Thus the slope of the highlight tone scale segment produced with this embodiment is not necessarily a monotonically decreasing function over the range from x_(ρ) to x_(he) even though each component function is a monotonically decreasing function. However, the slope of the highlight tone scale segment produced with this embodiment is a monotonically decreasing function over the range from x_(ρ) to x_(hc), i.e. the range of input pixel values that includes the intersection of the highlight and shadow tone scale segments. It is also possible, for this embodiment of the construction method, that the slope of the highlight tone scale segment be greater than 1.0 for the input pixel value domain in the vicinity of x_(he). This feature tends to maintain the appearance of specular highlights as bright spots even though the highlight tone scale segment is a compressive function, i.e. the range of input pixel values (x_(ρ) to x_(he)) is larger than the range of output pixel values (x_(ρ) to x_(we)).

In similar fashion, a shadow tone scale segment which achieves an inflection point can be constructed using more than one shadow component function. In another embodiment, a second shadow component function is constructed using the same functional form as expression (7) constrained to pass through the a specified coordinate point defined by an abscissa value x_(se) that results in a function value x_(be) as given by (46). x _(be)=α_(s2)(1−e ^(−(x) ^(se) ^(−x) ^(ρ) ^()/α) ^(s2) )+x _(ρ)  (46) This constraint achieves an extreme shadow black point mapping objective relating to the darkest part of the processed digital image. For the second shadow component function, the black point value x_(be) is predetermined based on the pixel value that is mapped by the rendering function R(x) to correspond to a black paper density of approximately 2.3, i.e. the maximum paper achievable paper density. The variable α_(s2) can be solved using the iterative numerical solution described above. The second shadow component function is described by expression (47). ƒ_(s2)(x)=α_(s2)(1−e ^(−(x−x) ^(ρ) ^()/α) ^(s2) )+x _(ρ)  (47) The shadow tone scale segment F_(s)(x) is constructed by combining the first shadow component function given by expression (22) with the second shadow component function given by expression (47). F_(S)(x) is given by expression (48) F _(s)(x)=ƒ_(s1)(x) for x _(ρ) <=x<=x _(sc) F _(s)(x)=ω_(s)(x)ƒ_(s1)(x)+(−ω_(s)(x))ƒ_(s2)(x) for x _(sc) <x<=x _(se) F _(s)(x)=ƒ_(s2)(x) for x _(se) <x  (48) where the function ω_(s)(x) represents a blending function of the two component functions and is given by expression (49). ω_(s)(x)=(x−x _(sc))/(x _(se) −x _(sc))  (49) The shadow tone scale segment so constructed consists of three input pixel domains. The first domain extends from a point defined by the variable x_(sc) to the reference gray point x_(ρ). This first shadow domain is constructed entirely from the first shadow component function ƒ_(s1)(x). The second shadow domain is constructed using a blend of the first and second shadow component functions and extends from the extreme shadow point x_(se) to the point x_(sc). The third shadow domain is constructed for the region for input pixel values less than the value of x_(se). As input pixel values decrease, denoted here by the variable x, approach x_(se), the shadow tone scale segment approaches the value of the second shadow component function ƒ_(s2)(x). Shadow tone scale segments constructed using expressions (48) can produce an inflection point in the function shape for some input pixel value x within the range from x_(se) to x_(ρ). That is, the slope of the shadow tone scale segment has a local minimum within the range from x_(se) to x_(ρ). Thus the slope of the shadow tone scale segment produced with this embodiment is not a monotonically increasing function over the range from x_(se) to x_(ρ) even though each component function is a monotonically increasing function. However, slope of the shadow tone scale segment produced with this embodiment is a monotonically increasing function over the range from x_(sc) to x_(ρ), i.e. the range of input pixel values that includes the intersection of the highlight and shadow tone scale segments. It is also possible for this embodiment of the construction method that the slope of the shadow tone scale segment be greater than 1.0 for the input pixel value domain in the vicinity of x_(se). This feature tends to maintain the appearance of deep shadows as very dark regions even though the shadow tone scale segment is a compressive function, i.e. the range of input pixel values (x_(se) to x_(ρ)) is larger than the range of output pixel values (x_(be) to x_(ρ)).

An example graph of the tone scale function 203 constructed using a highlight and tone scale segment each having been constructed from two component functions is shown in FIG. 13 a. The identity mapping one-to-one input pixel value-to-output pixel value line is indicated by line 560 and the reference gray point is indicated by point 561. The first, second, and third input pixel domains described above for the highlight tone scale segment are indicated by domains 562, 563, and 564 respectively. The abscissa and ordinate values for point 565 are the variables x_(he) and x_(we) respectively. The abscissa value for point 566 is the variable x_(he). The first, second, and third input pixel domains described above for the shadow tone scale segment are indicated by domains 567, 568, and 569 respectively. The abscissa and ordinate values for point 570 are the variables x_(se) and x_(be) respectively. The abscissa value for point 571 is the variable x_(sc).

The third highlight domain indicated by domain 564 relates to pixels with values greater than the variable x_(he). As will be described in more detail below, this domain of input pixel values relates to the brightest 0.1% pixels. Therefore, the shape of the third highlight domain really only affects a very small portion of the image area. The same argument can be made for the third shadow domain indicated by domain 569 relating to pixels with values less than the variable x_(se). Another approach for determining the shape of the third highlight domain that would produce acceptable results is to use the instantaneous slope of the tone scale function at the point 566 to project a straight line from point 566. Similarly, the instantaneous slope of the tone scale function at the point 570 can be used to project a straight line from point 570 for the third shadow domain.

FIG. 13 b shows the tone scale function 203 and its corresponding slope function. The identity mapping one-to-one input pixel value-to-output pixel value line is indicated by line 573 and the reference gray point is indicated by point 574. The tone scale function 203 shown in FIG. 13 a is shown in FIG. 13 b as curve 575. Several features of the slope function shown in FIG. 13 b are noteworthy. Point 579 indicates the slope function at the abscissa value equal to the reference gray point. The slope function is nearly discontinuous at the reference gray point and achieves a local maximum value at the reference gray point. The tone scale function 203 depicted in FIGS. 13 a and 13 b therefore has an inflection point at the abscissa value equal to the reference gray point since the corresponding slope function has a local maximum at the reference gray point. Point 577 indicates a local minimum of the slope function within the input pixel domain corresponding to the highlight tone scale segment. Thus the tone scale function 203 constructed with two highlight component functions can have an inflection point within the input pixel domain of the highlight tone scale segment. Similarly, point 578 indicates a local minimum of the slope function within the input pixel domain corresponding to the shadow tone scale segment. Thus the tone scale function 203 constructed with two shadow component functions can have an inflection point within the input pixel domain of the shadow tone scale segment.

The slope function shown in FIG. 13 b (indicated by curve 580) is shown in isolation in FIG. 13 c as indicated by curve 580 with the scale of the graph indicating the magnitude for the slope function. Thus it can be seen that the value of the slope function, which indicates the instantaneous slope of the tone scale function, is never negative, i.e. the tone scale function is a monotonic function. The tone scale function shown in FIGS. 13 a and 13 b is compressive for both the highlight and shadow tone scale segments. Thus the average value of the corresponding slope function depicted in FIG. 13 c is less than 1.0. The inflection points for the highlight and shadow tone scale segments are indicated as points 577 and 578 respectively. (These points are also indicated in FIG. 13 b.) The slope function achieves a relatively high value in the extreme highlight domain indicated by domain 581. The magnitude of the slope function in the domain 581 is about the same as the magnitude of the slope function for the mid-tone domain near the indicated point 579. The extreme shadow domain indicated by domain 582 achieves a slope which is higher even higher in magnitude. Therefore, the tone scale function depicted in FIGS. 13 a and 13 b achieves a relatively high slope at the reference gray point and within the extreme highlight and extreme shadow domains as well.

Referring to FIG. 4, the pixels of the source digital image, i.e. the input digital image to the tone scale module 330, can be used to determine the variables x_(ho) and x_(so) and thus determine the shape of the tone scale function. The analysis image generator 250 receives the source digital image and produces an analysis digital image 201 by applying a low-pass spatial filter and performing a sampling operation. The result is a lower spatial resolution version of the source digital image that has fewer pixels representing the same image content. Typical spatial resolutions for the analysis digital image 201 is approximately 64 by 96 pixels. The analysis digital image is also converted to be in a luminance-chrominance representation using expression (1).

The tone scale function generator 230 calculates the tone scale function 203 by analyzing the pixels of the analysis digital image 201. A pixel histogram function, i.e. a frequency of occurrence function, is calculated from the pixels of the luminance digital image channel of the analysis digital image 201. A cumulative histogram function is calculated from the pixel histogram function by integrating the values of the pixel histogram as a function of pixel value. FIG. 14 shows a graph of a example histogram function indicated by curve 601 and its corresponding cumulative histogram function indicated by curve 602. The cumulative histogram function is scaled between 0.0 and 1.0 and relates to the percentage area of the digital image with 1.0 corresponding to 100 percent of image area. The ordinate value of the cumulative histogram function relates to a given percentage image area Z. The corresponding abscissa value relates to the pixel value P for which the percentage of pixels in the image that have values less than P is given by Z. The 99.0% cumulative histogram function value is used to determine the value of the highlight point, variable x_(ho). The 1.0% cumulative histogram function value is used to determine the value of the shadow point, variable x_(so). The values for the variables x_(ho) and x_(so) are indicated as points 603 and 604 respectively on the graph shown in FIG. 14.

For the embodiment described above for which two highlight component functions are used to construct the highlight tone scale segment, the pixels of the source digital image can also be used to set the value of variable x_(he). The 99.9% cumulative histogram function value is used to determine the value of the extreme highlight point, variable x_(he). Similarly, for the embodiment described above for which two shadow component functions are used to the construct the shadow tone scale segment, the extreme shadow point variable x_(se) is determined using the 0.1% cumulative histogram function value. The values for the variables x_(he) and x_(se) are indicated as points 605 and 606 respectively on the graph shown in FIG. 14. Those skilled in the art will recognize that the present invention can be used with cumulative histogram percentile values other than the above mentioned values for the variables x_(so), x_(ho), x_(he), and x_(se) and still derive the benefits of the present invention. For optimum results, the variables x_(so) and x_(ho) should relate to relatively dark and light scene objects respectively and the variables x_(se) and x_(he) should relate to extremely dark and light scene objects respectively.

While the present invention uses the cumulative histogram function of image pixel values to determine variables x_(so), x_(ho), x_(he), and x_(se), masked cumulative histogram functions of image pixel values can also be used. For example, a spatial activity filter can be operated on the digital image to be processed and compared to a threshold value to produce an image mask. The image mask represents pixels that are included in the masked cumulative histogram function.

Other functions than the exponential functions described above can be used to generate the highlight and shadow component functions. For example, an integral-of a-Gaussian function can be used to construct the highlight and shadow component functions. A Gaussian function γ(x,σ) with a control parameter σ is given by (50). γ(u,σ)=e ^(−(u−x) ^(ρ) ⁾ ² ^(/2σ) ²   (50) The highlight component function ƒ_(h1)(x) is calculated as (51)

$\begin{matrix} {{f_{h1}(x)} = {{\left( {x_{we} - x_{\rho}} \right)\frac{\int_{x_{\rho}}^{x}{{\gamma\left( {u,\sigma_{H}} \right)}{\mathbb{d}u}}}{\int_{x_{\rho}}^{x_{MAX}}{{\gamma\left( {u,\sigma_{H}} \right)}{\mathbb{d}u}}}} + x_{\rho}}} & (51) \end{matrix}$ where x is defined for x>=x_(ρ) and the shadow component functions ƒ_(s1)(x) is calculated given (52) where x is define for x<=x_(ρ).

$\begin{matrix} {{f_{s1}(x)} = {{\left( {x_{\rho} - x_{be}} \right)\frac{\int_{x_{MIN}}^{x}{{\gamma\left( {u,\sigma_{S}} \right)}{\mathbb{d}u}}}{\int_{x_{MIN}}^{x_{\rho}}{{\gamma\left( {u,\sigma_{S}} \right)}{\mathbb{d}u}}}} + x_{be}}} & (52) \end{matrix}$ The integration procedure is performed using discrete values for the variable x to generate a look-up-table where x_(MIN) and X_(MAX) represent the minimum and maximum possible pixel values respectively. The variables σ_(H) and σ_(S) represent control parameters that determine the shape of the component functions. The variables σ_(H) and σ_(S) can be determined using expressions (53) and (54) respectively. σ_(H)=3.0(x _(he) −x _(ρ))  (53) σ_(S)=3.0(x _(ρ) −x _(se))  (54)

In another embodiment, a sigmoid shaped function is used to construct the highlight and shadow component functions. The highlight component θ_(h1)(x) is given by (55)

$\begin{matrix} {{f_{h1}(x)} = {{\left( {x_{we} - x_{\rho}} \right)\left( {\frac{2}{1 + {\mathbb{e}}^{{- x}\; K_{H}}} - 1} \right)} + x_{\rho}}} & (55) \end{matrix}$ and the shadow component ƒ_(s1)(x) is given by (56).

$\begin{matrix} {{f_{s1}(x)} = {{\left( {x_{\rho} - x_{be}} \right)\left( {\frac{2}{1 + {\mathbb{e}}^{{- x}\; K_{S}}} - 1} \right)} + x_{be}}} & (56) \end{matrix}$ The variables K_(H) and K_(S) can be independently selected to change the shape of the highlight and shadow component functions respectively. The variable K_(H) is determined such the ƒ_(h1)(x_(ho))=x_(w). The variable K_(S) is determined such the f_(s1)(x_(so))=x_(b).

It should be noted that many other functions in the general class of sigmoid functions can be used to generate the highlight and shadow component functions. A sigmoid shaped function refers herein to a function with the following properties: the first derivative of the function approaches a value of zero at the minimum and maximum values of the input domain, the function does not have a zero first derivative value between the minimum and maximum values of the input domain, and the function is either monotonically increasing or decreasing over the input domain. An important aspect of using a sigmoid shaped function for the highlight and shadow component functions is the independence of the two component functions. Therefore, a first sigmoid shaped function can be used for the highlight component function and a second sigmoid shaped function for the shadow component function. Although the sigmoid shaped function for the two component functions can have the same functional form, the first and second sigmoid shaped functions must have different control parameters otherwise the two sigmoid shaped functions would constitute a single sigmoid function. It will also be appreciated that tone scale functions constructed with two sigmoid shaped functions as described above will have a monotonically increasing slope for input pixel values below the reference gray point and have a monotonically decreasing slope for input pixel values above the reference gray point.

An important feature of the present invention is the use of two tone scale segments for constructing a tone scale function. Another important feature is the use of mathematical functions for constructing the two tone scale segments. In particular, a different mathematical function is used for the highlight tone scale segment than for the shadow tone scale segment. In the context of the present invention, different mathematical functions can share the same mathematical equation, i.e. the same mathematical combination of variables, however, the values of the control variables for the different mathematical functions must be different. For example, the highlight and shadow component function of expressions (13) and (14) respectively share the same mathematical equation but have different values for the control variable α denoted by α_(h1) and α_(s1) and are therefore considered different mathematical functions.

When the expressions (19) or (22) are used to construct a highlight or shadow tone scale segments respectively with just a single component function, the φ_(h) and φ_(s) variables can be selected independently. When the φ_(h) variable is selected to be 1.0, the highlight tone scale segment is described by a linear function. Similarly, when the φ_(s) variable is selected to be 1.0, the shadow tone scale segment is described by a linear function. The present invention preferably selects the values of the φ_(h) and φ_(s) variables to be 0.5. However, for some digital imaging system applications and depending on individual people's preferences, the values of the φ_(h) and φ_(s) variables can be selected to be 1.0 and yield very good results. This is largely due to the combination of the image independent sigmoid shaped rendering function R(x) employed by the rendering module 340 with the image dependent tone scale function 203 employed by the tone scale module 330. The use of the sigmoid shaped rendering function R(x) provides the graceful roll-off of pixel values at both extremes (light and dark extremes) that yields a photographically acceptable appearance to the processed digital images. With the variables φ_(h) and φ_(s) set to values less than 1.0, the constructed tone scale function 203 also achieves the property of a graceful roll-off of pixel values at both extremes. Therefore, another important feature of the present invention is the combination use of a sigmoid shaped image independent function with an image dependent two segment tone scale function.

Although the above description of the construction of the tone scale function has treated the underlying functions as continuous mathematical entities, those skilled in the art will recognize that for digital imaging applications, any function must be approximated by discrete values. Therefore, when implemented in a digital computer, the tone scale function 203 is actually represented by a collection of discrete values. The present invention uses a look-up-table to implement and store the tone scale function. To within the limits of a digital representation will allow, the tone scale function 203 is continuous. However, the tone scale function 203 can also be represented as a series of line segments wherein the points defining the line segments are determined using the mathematical functions described above. The main advantage of the method of using a series of line segment saves computation resources by using a linear interpolation method to generate the tone scale function points of the line segments.

Referring to FIG. 3 and FIG. 4, the tone scale function generator 230 can accept user input selections 231 in the generation of the tone scale function 203. For example a user of the system shown in FIG. 1 can view possible selections and/or control parameters on the monitor device 50 and indicate selections using the input control device 60 such as a keyboard or mouse pointing device. When used in this manual user mode, a highlight control parameter and a shadow control parameter are used to change the shape of the component functions that are used to generate the highlight tone scale segment and shadow tone scale segment respectively.

In a preferred embodiment for this manual mode of operation, expressions (44) and (48) are used to construct the highlight and shadow tone scale segments respectively. First the variables x_(ho), x_(he), x_(so) and x_(se) are determined automatically using the cumulative histogram values derived from the pixels of the source digital image 102. Next the variables α_(h1), α_(h2), α_(s1), and α_(s2) are determined by the iterative numerical solution described above. The tone scale function 203 is constructed from the highlight and shadow tone scale segments. Next the tone scale function 203 is applied to the source digital image 102 using the spatial filtering method described above. The user of the system views the resultant rendered digital image 103 either by viewing a photographic print generated with the processed digital image or by viewing the processed digital image on an electronic display device. After having analyzed the viewed image, the user can then indicate preferences as to the lightness of the shadow regions of the viewed image and the lightness of the highlight regions of the viewed image. The user makes selections by using the mouse pointing device with options having been displayed on an electronic display device via a graphical user interface. The user input selections 231 are used by the software implementation to change a highlight control parameter Δ_(h) and a shadow control parameter Δ_(s). The highlight and shadow control parameters are then used to modify the highlight and shadow tone scale segments of the tone scale function 203.

The variables Δ_(h) and Δ_(s) are first calculated using expressions (57) and (58) respectively. Δ_(h) =x _(w) −F _(H)(x _(w))  (57) Δ_(s) =x _(B) −F _(S)(x _(B))  (58) The user input selections are then used to change the values of the variables Δ_(h) and Δ_(s) resulting in variables Δ_(h)′ and Δ_(s)′. Next, the variable α_(h1) is recalculated subject to the constraint given by expression (59). F _(H)(x _(w))=x _(w)−Δ_(h)′  (59) Similarly, the variable α_(s1) is recalculated subject to the constraint given by expression (60). F _(S)(x _(S))=x _(S)−Δ_(s)′  (60) A modified tone scale function is constructed using the recalculated highlight and shadow tone scale segments. The modified tone scale function is then re-applied to the source digital image 102 and the resultant rendered digital image 103 is displayed on the electronic display device.

In an alternative embodiment of the manual user mode of operation, the highlight and shadow tone scale segments are set to defaults such that the tone scale function 203 assumes the identity mapping one-to-one input-to-output line shape. The user views a rendered digital image 103 on the electronic display device that essentially has not been enhanced to change its tone scale characteristics. The user then makes selections, as described above, to effect a change in the shadow and highlight lightness characteristics of the viewed image.

Having a tone scale segment with two inflection points can be advantageous for digital images that have, for at least a portion of the image area, pixels that relate to a no exposure condition. For example, digital images derived by scanning a photographic film strip can have image regions that correspond to photographic film densities that are indistinguishable from having received no light. These image regions have pixel values that correspond to the minimum film density. The minimum film density values can be obtained by scanning the inter-frame gap between adjacent image frames. A minimum density value is obtained as R_(min), G_(min), and B_(min) for the red, green, and blue digital image channel pixel data. These minimum density values are used to construct a third shadow component function. The resultant shadow tone scale segment exhibits two distinct inflection points.

The third shadow component function is calculated using a Gaussian function that maps pixel values corresponding to the minimum film density to the maximum achievable paper density (this is approximately a value of 2.3 as given in the example described above). Although the second shadow component function can also map the pixel values corresponding to the minimum film density to the maximum achievable paper density, the third shadow component function maps a greater range of pixel values to the maximum achievable paper density. This property of the third shadow component function ensures that any noise associated with the minimum film density response is also mapped to the maximum achievable paper density. It should also be noted that the minimum film density relates to a condition of receiving no light. Therefore, the third shadow component function achieves a strategy of mapping a response value that relates to a condition of having received no light to a maximum paper density.

First a black level (denoted by the variable x_(BLK)) is calculated as the average of the variables R_(min), G_(min), and B_(min). plus a margin for noise given by the expression (61). x _(BLK)=α_(N)σ_(N)+(R _(min) +G _(min) B _(min))/3  (61) The variable σ_(N) represents the noise standard deviation as measured in the pixel value range corresponding to the film minimum density. (This quantity can also be measured from pixels within the inter-frame gaps.) The variable α_(N) represents a noise magnitude scaling factor that is set based on the level of noise suppression desired and is preferable set to a value of between 1.0 and 2.0. The expression for the third shadow component function is given by (62) ƒ_(s3)(x)=α_(BLK) e ^(−(x−x) ^(BLK) ⁾ ² ^(/2σ) ^(BLK) ² for x>x _(BLK) ƒ_(s3)(x)=0.0 for x<=x _(BLK)  (62) where the variable σ_(BLK) determines the influence of the Gaussian function and the variable α_(BLK) determines the magnitude of the effect. The shadow tone scale segment is calculated by numerically subtracting the third shadow component function to the expression (48). The final expression for the shadow tone scale segment using three component functions is given by expression (63). F _(s)(x)=ƒ_(s1)(x) for x _(ρ) <=x<=x _(sc) F _(s)(x)=ω_(s)(x)ƒ_(s1)(x)+(1−ω_(s)(x))ƒ_(s2)(x) for x _(sc) <x<=x _(se) F _(s)(x)=ƒ_(s2)(x)−ƒ_(s3)(x) for x _(sc) <x  (63)

FIG. 15 shows an example shadow tone scale segment (indicated by curve 607) illustrating the shape of the third component function combined with the shadow tone scale segment given by ƒ_(s2)(x) and inflection point 608. The variable x_(BLK) for the example function shown in FIG. 15 was 788, the reference gray point was 1618, the value for the variable σ_(BLK) was 113, and the value of the variable α_(BLK) was 376.

FIG. 16 shows an example tone scale function (curve 609) generated with the present invention. The highlight tone scale segment has two inflection points shown by points 613 and 611. The corresponding slope function, indicated by curve 610, has a local slope minimum and maximum indicated by points 614 and 612 that correspond to function points 613 and 611 respectively. The shadow tone scale segment has three inflection points indicated by points 623, 621, 619, and 617. The corresponding slope function has a local slope minimum indicated by points 622 and 618 that correspond to function points 621 and 617 respectively. The slope function has a local slope maximum indicated by points 624 and 620 that correspond to function points 623 and 619 respectively.

Referring to FIG. 16, the tone scale function, indicated by curve 609, also achieves an inflection point where the two tone scale segments meet, i.e. at the point indicated by 615. The corresponding tone scale function, curve 610, has a local maximum as indicated by point 616. The shape of the highlight and shadow tone scale segments can be independently selected. Therefore, the tone scale function shown in FIG. 15 is an example of a tone scale function that achieves an inflection point constructed from two independently controllable tone scale segments.

An important feature of the tone scale functions produced with the method of the present invention is the occurrence of inflection points within the domain of a tone scale function. The present invention generates a tone scale function from two connected tone scale segments each of which can independently have at least one characteristic inflection point. Inflection points are caused by a change in the local slope characteristics of a tone scale function. These changes in the corresponding slope function are generally a consequence of the black and white point mapping constraints applied and the choice of functions used in the tone scale function construction. In particular, the inflection point exhibited within the highlight tone scale segment is a consequence of imposing the simultaneous constraints of 1) maintaining the reference gray point x_(ρ), 2) mapping a first image histogram fixed percentile value (x_(ho) set to the 99.0% cumulative histogram function value) to the white point value x_(w), 3) mapping a second image histogram fixed percentile value (x_(he) set to the 99.9% cumulative histogram function value) to the extreme white point value x_(we), and using two or more functions to construct the tone scale segment. Similarly, the inflection point exhibited within the shadow tone scale segment is a consequence of imposing the simultaneous constraints of 1) maintaining the reference gray point x_(ρ), 2) mapping a first image histogram fixed percentile value (x_(so) set to the 1.0% cumulative histogram function value) to the black point value x_(b), 3) mapping a second image histogram fixed percentile value (x_(se) set to the 0.1% cumulative histogram function value) to the extreme black point value x_(be), and using two or more functions to construct the tone scale segment.

Therefore, satisfying the simultaneous constraints of mapping two or more image histogram fixed percentile values to two different white point values can produce an inflection point within the highlight domain of the tone scale function. The inflection point feature of the tone scale function is sometimes necessary to achieve the bright white visual appearance of recorded specular highlights (mapped to the extreme white point value) while simultaneously achieving the darker than bright white appearance of other recorded bright scene content. For example, in a given scene, a yellow flower can represent a bright object that is mapped to the white point x_(w) thus achieving a bright yellow appearance. By mapping the average pixel value corresponding to the yellow flower scene content to a value that is less than the brightest value achievable by the photographic paper, the spatial modulation within the yellow flower image region can be numerically expressed, and therefore, visually appreciated. In the same scene, the brightest portions of white cloud scene content, or the glint reflection off a shinny surface, are mapped to the extreme white point x_(we) thus achieving the bright white visual appearance. If instead only one of these two white point mapping constraints were satisfied, the resultant tone scale function would not achieve an inflection point and the visual appearance of either the specular highlights or the yellow flower detail would be sacrificed. Thus it will be appreciated that the inflection point feature within the highlight tone scale segment can be important for impressive visual results when viewing the processed digital images.

A similar argument can be made for the shadow tone scale segment. Satisfying the simultaneous constraints of mapping two or more image histogram fixed percentile values to two different black point values can produce an inflection point within the shadow domain of the tone scale function. The extreme shadows of images, such as the deep shadows of a forest scene or the dark background of a night flash scene, are optimally mapped to the extreme black point x_(be) corresponding to the highest achievable paper density. This mapping strategy produces visually pleasing results since the darkest image regions appear black on the photographic print. However, other shadow scene content that may relate to the main subject, such as a back lighted face or dark clothing, may need to be mapped to a pixel value that is numerically higher than the extreme black point to enable the visualization of image spatial detail. Therefore, for optimal shadow scene content rendering, an inflection point (resulting from the two point mapping constraints) within the shadow tone scale segment is an important feature of the tone scale function.

It should also be noted that the inflection point feature of the tone scale function achieved at the reference gray point is similarly a consequence of simultaneously applying the black and white point mapping constraints. Thus it will also be appreciated that the tone scale functions produced with the present invention achieve an inflection point at the reference gray point as well as within the highlight and shadow tone scale segments.

Another important feature of the tone scale functions produced with the present invention is the independence of the inflection points achieved in the highlight and shadow tone scale segments. Depending on the image histogram data, one or both of the these tone scale segments may not achieve an inflection point. For example, if the shape of the image histogram is such that the 99.9% cumulative value is much larger than the 99.0% cumulative value, the slope of the highlight tone scale segment may not reverse sign and thus no inflection point will be exhibited. Similarly, if the shape of the image histogram is such that the 0.1% cumulative value is much less than the 1.0% cumulative value, the slope of the shadow tone scale segment may not reverse sign and thus no inflection point will be exhibited. However, the numerical values for the 0.1% and 1.0% cumulative values are independent of the values for the 99.0% and 99.9% cumulative values for an arbitrary digital image. Thus, tone scale functions produced with the present invention can exhibit an inflection point for the shadow tone scale segment but may not exhibit an inflection point for the highlight tone scale segment. Similarly, the tone scale function can have an inflection point for the highlight tone scale segment but not for the shadow tone scale segment. Therefore, the present invention facilitates using tone scale functions to improve the rendition of highlight and shadow regions wherein the shape of the tone scale function achieves an inflection point independently for highlight and shadow image regions and such inflection points are independently controllable of each other.

Those skilled in the art will recognize that histogram equalization methods can also produce inflection points within the domain of a tone scale function. However, the tone scale functions generated with histogram equalization methods constitute a single mathematical formulation since the statistics of the image pixel values dictate the shape of the tone scale function. This property is true even for constrained histogram equalization methods such as disclosed by Alkofer in U.S. Pat. No. 4,731,671 and by Kwon in commonly-assigned U.S. Pat. No. 4,745,465. The present invention uses a small number of fixed percentile cumulative histogram values and component functions to control the shape of the tone scale function. In one embodiment, four fixed percentile cumulative histogram values are used. Therefore, it is possible that two different digital images can have the same four fixed cumulative histogram values but have very different overall cumulative histogram function shapes. However, the tone scale function generated with the present invention for these two digital images will be the same whereas a histogram equalization method will result in very differently shaped tone scale functions for these two different digital images.

The present invention is preferably practiced in an image processing system including a source of digital images, such as a scanner, a computer programmed to process digital images, and an output device such as a thermal or inkjet printer. The method of the present invention may be sold as a computer program product including a computer readable storage medium bearing computer code for implementing the steps of the invention.

Computer readable storage medium may include, for example; magnetic storage media such as a magnetic disc (e.g. a floppy disc) or magnetic tape; optical storage media such as optical disc or optical tape; bar code; solid state electronic storage devices such as random access memory (RAM) or read only memory (ROM); or any other physical device or medium employed to store a computer program.

The invention has been described in detail with particular reference to certain preferred embodiments thereof, but it will be understood that variations and modifications can be effected within the spirit and scope of the invention.

PARTS LIST

-   10 image capture device -   10 a image capture device -   10 b image capture device -   10 c image capture device -   20 digital image processor -   30 a image output device -   30 b image output device -   40 general control processor -   50 monitor device -   60 input control device -   70 computer memory device -   101 original digital image -   102 source digital image -   103 rendered digital image -   104 enhanced digital image -   107 luminance digital image -   109 chrominance digital image -   113 enhanced luminance digital image -   201 analysis digital image -   203 tone scale function -   210 LCC conversion module -   220 RGB conversion module -   230 tone scale function generator -   231 user input selections -   240 tone scale function applicator -   250 analysis image generator     Parts List Cont'd -   310 RLSE conversion module -   320 scene balance module -   330 tone scale module -   340 rendering module -   401 input digital image -   402 texture digital image -   403 pedestal digital image -   407 tone scale adjusted digital image -   409 output digital image -   410 pedestal generation module -   420 difference module -   430 tone scale pedestal applicator -   440 addition module -   500 point -   501 curve -   502 curve -   503 line -   511 point -   512 curve -   513 curve -   514 point -   515 curve -   516 curve -   517 curve -   518 curve -   519 point -   521 point -   522 curve     Parts List Cont'd -   523 curve -   524 line -   530 curve -   531 line -   532 curve -   533 point -   534 point -   535 line -   536 curve -   537 curve -   538 point -   539 curve -   540 line -   541 point -   542 line -   543 curve -   544 curve -   549 point -   550 curve -   551 curve -   552 line -   553 curve -   554 curve -   555 curve -   556 curve -   557 line -   558 curve -   559 curve -   560 line     Parts List Cont'd -   561 point -   562 domain -   563 domain -   564 domain -   565 point -   566 point -   567 domain -   568 domain -   569 domain -   570 point -   571 point -   573 line -   574 point -   575 curve -   577 point -   578 point -   579 point -   580 curve -   581 domain -   582 domain -   590 curve -   591 point -   592 point -   593 point -   595 point -   596 point -   597 point -   598 curve     Parts List Cont'd -   601 curve -   602 curve -   603 point -   604 point -   605 point -   606 point -   607 curve -   608 point -   609 curve -   610 curve -   611 point -   612 point -   613 point -   614 point -   615 point -   616 point -   617 point -   618 point -   619 point -   620 curve -   621 point -   622 point -   623 point -   624 point 

1. A method of producing a tone scale function which can operate on a source digital image to improve tonal characteristics, comprising the steps of: a) receiving a source digital image including a plurality of pixels; and b) producing a tone scale function having a highlight tone scale segment and a shadow tone scale segment defined relative to a reference point on the tone scale function, and that is adapted to operate on the source digital image to improve its tonal characteristics, wherein: i) the highlight tone scale segment is defined by a different mathematical function than the shadow tone scale segment; ii) at least one of the highlight tone scale or shadow tone scale segments have at least one inflection point; iii) the highlight tone scale segment is defined for points that are equal to or greater than the reference point; and iv) the shadow tone scale segment is defined for points that are equal to or less than the reference point.
 2. The method of claim 1 using the tone scale function and the source digital image to produce an enhanced digital image.
 3. The method of claim 2 using a sigmoid shaped rendering function independent of the source digital image and the enhanced digital image to produce a rendered digital image.
 4. The method of claim 1 wherein the highlight tone scale segment is manually controlled with at least one highlight control parameter and the shadow tone scale segment is manually controlled with at least one shadow control parameter.
 5. The method of claim 1 wherein both the highlight and shadow tone scale segments have an inflection point.
 6. The method of claim 1 wherein the shadow tone scale segment has at least two inflection points.
 7. The method of claim 1 wherein at least two exponential functions are used to define the highlight tone scale segment.
 8. The method of claim 1 wherein at least two exponential functions are used to define the shadow tone scale segment.
 9. The method of claim 1 wherein the slopes of the highlight and shadow tone scale segments are each equal at the reference point.
 10. The method of claim 1 wherein the slopes of the highlight and shadow tone scale segments are unequal at the reference point.
 11. The method of claim 1 further including the step of using the pixels of the source digital image to produce the tone scale function.
 12. The method of claim 1 further including the step of using the pixels of the source digital image to produce a histogram and using the histogram to produce the tone scale function.
 13. The method of claim 12 wherein the highlight tone scale segment maps a fixed percentile of the histogram to a predetermined output value corresponding to a first paper density and the shadow tone scale segment maps a fixed percentile of the histogram to a predetermined output value corresponding to a second paper density.
 14. The method of claim 1 wherein the source digital image is in a luminance-chrominance representation.
 15. The method of claim 1 wherein the source digital image includes pixels having at least three different color digital image channels.
 16. The method of claim 1 wherein the pixels of the source digital image have a logarithmic relationship to original scene intensities.
 17. The method of claim 2 further including using a spatial filter in applying the tone scale function to the source digital image.
 18. The method of claim 1 wherein the mathematical function used to define the highlight tone scale segment employs: a first function with a monotonically decreasing slope characteristic that includes points that are equal to or greater than the reference point; and a second function with a monotonically decreasing slope characteristic that includes points that are equal to or greater than the reference point.
 19. The method of claim 18 further including the step of using the pixels of the source digital image to produce a histogram and using the histogram to produce the highlight tone scale segment.
 20. The method of claim 19 wherein the highlight tone scale segment maps: a first fixed percentile of the histogram to a predetermined output value corresponding to a first paper density; and a second fixed percentile of the histogram to a predetermined output value corresponding to a second paper density.
 21. The method of claim 18 wherein the mathematical function used to define the highlight tone scale segment employs: a linear function wherein the contribution of the linear function to the highlight tone scale segment is selected by at least one controllable parameter.
 22. The method of claim 21 further including the step of using the pixels of the source digital image to produce a histogram and using the histogram to produce the highlight tone scale segment.
 23. The method of claim 22 wherein the highlight tone scale segment maps: a first fixed percentile of the histogram to a predetermined output value corresponding to a first paper density; and a second fixed percentile of the histogram to a predetermined output value corresponding to a second paper density.
 24. The method of claim 1 wherein the mathematical function used to define the shadow tone scale segment employs: a first function with a monotonically increasing slope characteristic that includes points that are equal to or less than the reference point; and a second function with a monotonically increasing slope characteristic that includes points that are equal to or less than the reference point.
 25. The method of claim 24 further including the step of using the pixels of the source digital image to produce a histogram and using the histogram to produce the shadow tone scale segment.
 26. The method of claim 25 wherein the shadow tone scale segment maps: a first fixed percentile of the histogram to a predetermined output value corresponding to a first paper density; and a second fixed percentile of the histogram to a predetermined output value corresponding to a second paper density.
 27. The method of claim 24 wherein the mathematical function used to define the shadow tone scale segment employs: a linear function wherein the contribution of the linear function to the shadow tone scale segment is selected by at least one controllable parameter.
 28. The method of claim 27 further including the step of using the pixels of the source digital image to produce a histogram and using the histogram to produce the shadow tone scale segment.
 29. The method of claim 28 wherein the shadow tone scale segment maps: a first fixed percentile of the histogram to a predetermined output value corresponding to a first paper density; and a second fixed percentile of the histogram to a predetermined output value corresponding to a second paper density.
 30. The method of claim 1 wherein the shadow tone scale segment maps a pixel value corresponding to a no exposure condition to a maximum paper density.
 31. The method of claim 1 wherein the reference point is calculated using the pixels of the source digital image.
 32. A computer storage product having at least one computer storage medium having instructions stored therein causing one or more computers to perform the method of claim
 1. 